De-centralized load-balancing at processors

ABSTRACT

A mechanism is described for facilitating localized load-balancing for processors in computing devices. A method of embodiments, as described herein, includes facilitating hosting, at a processor of a computing device, a local load-balancing mechanism. The method may further include monitoring balancing of loads at the processor and serving as a local scheduler to maintain de-centralized load-balancing at the processor and between the processor and other one or more processors.

RELATED APPLICATION

The present application is a continuation of and claims the benefit of U.S. patent application Ser. No. 15/477,025, filed on Apr. 1, 2017 and entitled “DE-CENTRALIZED LOAD-BALANCING AT PROCESSORS”, which is incorporated by reference in its entirety.

FIELD

Embodiments described herein relate generally to data processing and more particularly to facilitate de-centralized load-balancing at processors.

BACKGROUND

Current parallel graphics data processing includes systems and methods developed to perform specific operations on graphics data such as, for example, linear interpolation, tessellation, rasterization, texture mapping, depth testing, etc. Traditionally, graphics processors used fixed function computational units to process graphics data; however, more recently, portions of graphics processors have been made programmable, enabling such processors to support a wider variety of operations for processing vertex and fragment data.

To further increase performance, graphics processors typically implement processing techniques such as pipelining that attempt to process, in parallel, as much graphics data as possible throughout the different parts of the graphics pipeline. Parallel graphics processors with single instruction, multiple thread (SIMT) architectures are designed to maximize the amount of parallel processing in the graphics pipeline. In an SIMT architecture, groups of parallel threads attempt to execute program instructions synchronously together as often as possible to increase processing efficiency. A general overview of software and hardware for SIMT architectures can be found in Shane Cook, CUDA Programming, Chapter 3, pages 37-51 (2013) and/or Nicholas Wilt. CUDA Handbook, A Comprehensive Guide to GPU Programming, Sections 2.6.2 to 3.1.2 (June 2013).

Conventional techniques do not provide for uniform load-balancing across graphics processor and thus, such techniques lead to performance inefficiency.

BRIEF DESCRIPTION OF THE DRAWINGS

Embodiments are illustrated by way of example, and not by way of limitation, in the figures of the accompanying drawings in which like reference numerals refer to similar elements. So that the manner in which the above recited features can be understood in detail, a more particular description, briefly summarized above, may be had by reference to embodiments, some of which are illustrated in the appended drawings. It is to be noted, however, that the appended drawings illustrate only typical embodiments and are therefore not to be considered limiting of its scope, for the drawings may illustrate other equally effective embodiments.

FIG. 1 is a block diagram illustrating a computer system configured to implement one or more aspects of the embodiments described herein.

FIG. 2A-2D illustrate a parallel processor components, according to an embodiment.

FIG. 3A-3B are block diagrams of graphics multiprocessors, according to embodiments.

FIG. 4A-4F illustrate an exemplary architecture in which a plurality of graphics processing units are communicatively coupled to a plurality of multi-core processors.

FIG. 5 is a conceptual diagram of a graphics processing pipeline, according to an embodiment.

FIG. 6 illustrates a computing device hosting a de-centralized load-balancing scheduling mechanism according to one embodiment.

FIG. 7 illustrates a de-centralized load-balancing mechanism according to one embodiment.

FIG. 8 illustrates a conventional framework employing a centralized scheduler.

FIG. 9A illustrates a novel architectural placement employing a de-centralized load-balancing mechanism according to one embodiment.

FIG. 9B illustrates a novel architectural placement employing a de-centralized load-balancing mechanism according to one embodiment.

FIG. 9C illustrates shared local memory de-fragmentation operation according to one embodiment.

FIG. 10 is a block diagram of an embodiment of a computer system with a processor having one or more processor cores and graphics processors.

FIG. 11 is a block diagram of one embodiment of a processor having one or more processor cores, an integrated memory controller, and an integrated graphics processor.

FIG. 12 is a block diagram of one embodiment of a graphics processor which may be a discreet graphics processing unit, or may be graphics processor integrated with a plurality of processing cores.

FIG. 13 is a block diagram of an embodiment of a graphics processing engine for a graphics processor.

FIG. 14 is a block diagram of another embodiment of a graphics processor.

FIG. 15 is a block diagram of thread execution logic including an array of processing elements.

FIG. 16 illustrates a graphics processor execution unit instruction format according to an embodiment.

FIG. 17 is a block diagram of another embodiment of a graphics processor which includes a graphics pipeline, a media pipeline, a display engine, thread execution logic, and a render output pipeline.

FIG. 18A is a block diagram illustrating a graphics processor command format according to an embodiment.

FIG. 18B is a block diagram illustrating a graphics processor command sequence according to an embodiment.

FIG. 19 illustrates exemplary graphics software architecture for a data processing system according to an embodiment.

FIG. 20 is a block diagram illustrating an IP core development system that may be used to manufacture an integrated circuit to perform operations according to an embodiment.

FIG. 21 is a block diagram illustrating an exemplary system on a chip integrated circuit that may be fabricated using one or more IP cores, according to an embodiment.

FIG. 22 is a block diagram illustrating an exemplary graphics processor of a system on a chip integrated circuit.

FIG. 23 is a block diagram illustrating an additional exemplary graphics processor of a system on a chip integrated circuit.

DETAILED DESCRIPTION

In some embodiments, a graphics processing unit (GPU) is communicatively coupled to host/processor cores to accelerate graphics operations, machine-learning operations, pattern analysis operations, and various general purpose GPU (GPGPU) functions. The GPU may be communicatively coupled to the host processor/cores over a bus or another interconnect (e.g., a high-speed interconnect such as PCIe or NVLink). In other embodiments, the GPU may be integrated on the same package or chip as the cores and communicatively coupled to the cores over an internal processor bus/interconnect (i.e., internal to the package or chip). Regardless of the manner in which the GPU is connected, the processor cores may allocate work to the GPU in the form of sequences of commands/instructions contained in a work descriptor. The GPU then uses dedicated circuitry/logic for efficiently processing these commands/instructions.

In the following description, numerous specific details are set forth. However, embodiments, as described herein, may be practiced without these specific details. In other instances, well-known circuits, structures and techniques have not been shown in details in order not to obscure the understanding of this description.

System Overview I

FIG. 1 is a block diagram illustrating a computing system 100 configured to implement one or more aspects of the embodiments described herein. The computing system 100 includes a processing subsystem 101 having one or more processor(s) 102 and a system memory 104 communicating via an interconnection path that may include a memory hub 105. The memory hub 105 may be a separate component within a chipset component or may be integrated within the one or more processor(s) 102. The memory hub 105 couples with an I/O subsystem 111 via a communication link 106. The I/O subsystem 111 includes an I/O hub 107 that can enable the computing system 100 to receive input from one or more input device(s) 108. Additionally, the I/O hub 107 can enable a display controller, which may be included in the one or more processor(s) 102, to provide outputs to one or more display device(s) 110A. In one embodiment, the one or more display device(s) 110A coupled with the I/O hub 107 can include a local, internal, or embedded display device.

In one embodiment, the processing subsystem 101 includes one or more parallel processor(s) 112 coupled to memory hub 105 via a bus or other communication link 113. The communication link 113 may be one of any number of standards based communication link technologies or protocols, such as, but not limited to PCI Express, or may be a vendor specific communications interface or communications fabric. In one embodiment, the one or more parallel processor(s) 112 form a computationally focused parallel or vector processing system that an include a large number of processing cores and/or processing clusters, such as a many integrated core (MIC) processor. In one embodiment, the one or more parallel processor(s) 112 form a graphics processing subsystem that can output pixels to one of the one or more display device(s) 110A coupled via the I/O Hub 107. The one or more parallel processor(s) 112 can also include a display controller and display interface (not shown) to enable a direct connection to one or more display device(s) 110B.

Within the I/O subsystem 111, a system storage unit 114 can connect to the I/O hub 107 to provide a storage mechanism for the computing system 100. An I/O switch 116 can be used to provide an interface mechanism to enable connections between the I/O hub 107 and other components, such as a network adapter 118 and/or wireless network adapter 119 that may be integrated into the platform, and various other devices that can be added via one or more add-in device(s) 120. The network adapter 118 can be an Ethernet adapter or another wired network adapter. The wireless network adapter 119 can include one or more of a Wi-Fi, Bluetooth, near field communication (NFC), or other network device that includes one or more wireless radios.

The computing system 100 can include other components not explicitly shown, including USB or other port connections, optical storage drives, video capture devices, and the like, may also be connected to the I/O hub 107. Communication paths interconnecting the various components in FIG. 1 may be implemented using any suitable protocols, such as PCI (Peripheral Component Interconnect) based protocols (e.g., PCI-Express), or any other bus or point-to-point communication interfaces and/or protocol(s), such as the NV-Link high-speed interconnect, or interconnect protocols known in the art.

In one embodiment, the one or more parallel processor(s) 112 incorporate circuitry optimized for graphics and video processing, including, for example, video output circuitry, and constitutes a graphics processing unit (GPU). In another embodiment, the one or more parallel processor(s) 112 incorporate circuitry optimized for general purpose processing, while preserving the underlying computational architecture, described in greater detail herein. In yet another embodiment, components of the computing system 100 may be integrated with one or more other system elements on a single integrated circuit. For example, the one or more parallel processor(s), 112 memory hub 105, processor(s) 102, and I/O hub 107 can be integrated into a system on chip (SoC) integrated circuit. Alternatively, the components of the computing system 100 can be integrated into a single package to form a system in package (SIP) configuration. In one embodiment, at least a portion of the components of the computing system 100 can be integrated into a multi-chip module (MCM), which can be interconnected with other multi-chip modules into a modular computing system.

It will be appreciated that the computing system 100 shown herein is illustrative and that variations and modifications are possible. The connection topology, including the number and arrangement of bridges, the number of processor(s) 102, and the number of parallel processor(s) 112, may be modified as desired. For instance, in some embodiments, system memory 104 is connected to the processor(s) 102 directly rather than through a bridge, while other devices communicate with system memory 104 via the memory hub 105 and the processor(s) 102. In other alternative topologies, the parallel processor(s) 112 are connected to the I/O hub 107 or directly to one of the one or more processor(s) 102, rather than to the memory hub 105. In other embodiments, the I/O hub 107 and memory hub 105 may be integrated into a single chip. Some embodiments may include two or more sets of processor(s) 102 attached via multiple sockets, which can couple with two or more instances of the parallel processor(s) 112.

Some of the particular components shown herein are optional and may not be included in all implementations of the computing system 100. For example, any number of add-in cards or peripherals may be supported, or some components may be eliminated. Furthermore, some architectures may use different terminology for components similar to those illustrated in FIG. 1. For example, the memory hub 105 may be referred to as a Northbridge in some architectures, while the I/O hub 107 may be referred to as a Southbridge.

FIG. 2A illustrates a parallel processor 200, according to an embodiment. The various components of the parallel processor 200 may be implemented using one or more integrated circuit devices, such as programmable processors, application specific integrated circuits (ASICs), or field programmable gate arrays (FPGA). The illustrated parallel processor 200 is a variant of the one or more parallel processor(s) 112 shown in FIG. 1, according to an embodiment.

In one embodiment, the parallel processor 200 includes a parallel processing unit 202. The parallel processing unit includes an I/O unit 204 that enables communication with other devices, including other instances of the parallel processing unit 202. The I/O unit 204 may be directly connected to other devices. In one embodiment, the I/O unit 204 connects with other devices via the use of a hub or switch interface, such as memory hub 105. The connections between the memory hub 105 and the I/O unit 204 form a communication link 113. Within the parallel processing unit 202, the I/O unit 204 connects with a host interface 206 and a memory crossbar 216, where the host interface 206 receives commands directed to performing processing operations and the memory crossbar 216 receives commands directed to performing memory operations.

When the host interface 206 receives a command buffer via the I/O unit 204, the host interface 206 can direct work operations to perform those commands to a front end 208. In one embodiment, the front end 208 couples with a scheduler 210, which is configured to distribute commands or other work items to a processing cluster array 212. In one embodiment, the scheduler 210 ensures that the processing cluster array 212 is properly configured and in a valid state before tasks are distributed to the processing clusters of the processing cluster array 212.

The processing cluster array 212 can include up to “N” processing clusters (e.g., cluster 214A, cluster 214B, through cluster 214N). Each cluster 214A-214N of the processing cluster array 212 can execute a large number of concurrent threads. The scheduler 210 can allocate work to the clusters 214A-214N of the processing cluster array 212 using various scheduling and/or work distribution algorithms, which may vary depending on the workload arising for each type of program or computation. The scheduling can be handled dynamically by the scheduler 210, or can be assisted in part by compiler logic during compilation of program logic configured for execution by the processing cluster array 212.

In one embodiment, different clusters 214A-214N of processing cluster array 212 can be allocated for processing different types of programs or for performing different types of computations.

The processing cluster array 212 can be configured to perform various types of parallel processing operations. In one embodiment, the processing cluster array 212 is configured to perform general-purpose parallel compute operations. For example, the processing cluster array 212 can include logic to execute processing tasks including filtering of video and/or audio data, performing modeling operations, including physics operations, and performing data transformations.

In one embodiment, the processing cluster array 212 is configured to perform parallel graphics processing operations. In embodiments in which the parallel processor 200 is configured to perform graphics processing operations, the processing cluster array 212 can include additional logic to support the execution of such graphics processing operations, including, but not limited to texture sampling logic to perform texture operations, as well as tessellation logic and other vertex processing logic. Additionally, the processing cluster array 212 can be configured to execute graphics processing related shader programs such as, but not limited to vertex shaders, tessellation shaders, geometry shaders, and pixel shaders. The parallel processing unit 202 can transfer data from system memory via the I/O unit 204 for processing. During processing the transferred data can be stored to on-chip memory (e.g., parallel processor memory 222) during processing, then written back to system memory.

In one embodiment, when the parallel processing unit 202 is used to perform graphics processing, the scheduler 210 can be configured to divide the processing workload into approximately equal sized tasks, to better enable distribution of the graphics processing operations to multiple clusters 214A-214N of the processing cluster array 212. In some embodiments, portions of the processing cluster array 212 can be configured to perform different types of processing. For example, a first portion may be configured to perform vertex shading and topology generation, a second portion may be configured to perform tessellation and geometry shading, and a third portion may be configured to perform pixel shading or other screen space operations, to produce a rendered image for display. Intermediate data produced by one or more of the clusters 214A-214N may be stored in buffers to allow the intermediate data to be transmitted between clusters 214A-214N for further processing.

During operation, the processing cluster array 212 can receive processing tasks to be executed via the scheduler 210, which receives commands defining processing tasks from front end 208. For graphics processing operations, processing tasks can include indices of data to be processed, e.g., surface (patch) data, primitive data, vertex data, and/or pixel data, as well as state parameters and commands defining how the data is to be processed (e.g., what program is to be executed). The scheduler 210 may be configured to fetch the indices corresponding to the tasks or may receive the indices from the front end 208. The front end 208 can be configured to ensure the processing cluster array 212 is configured to a valid state before the workload specified by incoming command buffers (e.g., batch-buffers, push buffers, etc.) is initiated.

Each of the one or more instances of the parallel processing unit 202 can couple with parallel processor memory 222. The parallel processor memory 222 can be accessed via the memory crossbar 216, which can receive memory requests from the processing cluster array 212 as well as the I/O unit 204. The memory crossbar 216 can access the parallel processor memory 222 via a memory interface 218. The memory interface 218 can include multiple partition units (e.g., partition unit 220A, partition unit 220B, through partition unit 220N) that can each couple to a portion (e.g., memory unit) of parallel processor memory 222. In one implementation, the number of partition units 220A-220N is configured to be equal to the number of memory units, such that a first partition unit 220A has a corresponding first memory unit 224A, a second partition unit 220B has a corresponding memory unit 224B, and an Nth partition unit 220N has a corresponding Nth memory unit 224N. In other embodiments, the number of partition units 220A-220N may not be equal to the number of memory devices.

In various embodiments, the memory units 224A-224N can include various types of memory devices, including dynamic random access memory (DRAM) or graphics random access memory, such as synchronous graphics random access memory (SGRAM), including graphics double data rate (GDDR) memory. In one embodiment, the memory units 224A-224N may also include 3D stacked memory, including but not limited to high bandwidth memory (HBM). Persons skilled in the art will appreciate that the specific implementation of the memory units 224A-224N can vary, and can be selected from one of various conventional designs. Render targets, such as frame buffers or texture maps may be stored across the memory units 224A-224N, allowing partition units 220A-220N to write portions of each render target in parallel to efficiently use the available bandwidth of parallel processor memory 222. In some embodiments, a local instance of the parallel processor memory 222 may be excluded in favor of a unified memory design that utilizes system memory in conjunction with local cache memory.

In one embodiment, any one of the clusters 214A-214N of the processing cluster array 212 can process data that will be written to any of the memory units 224A-224N within parallel processor memory 222. The memory crossbar 216 can be configured to transfer the output of each cluster 214A-214N to any partition unit 220A-220N or to another cluster 214A-214N, which can perform additional processing operations on the output. Each cluster 214A-214N can communicate with the memory interface 218 through the memory crossbar 216 to read from or write to various external memory devices. In one embodiment, the memory crossbar 216 has a connection to the memory interface 218 to communicate with the I/O unit 204, as well as a connection to a local instance of the parallel processor memory 222, enabling the processing units within the different processing clusters 214A-214N to communicate with system memory or other memory that is not local to the parallel processing unit 202. In one embodiment, the memory crossbar 216 can use virtual channels to separate traffic streams between the clusters 214A-214N and the partition units 220A-220N.

While a single instance of the parallel processing unit 202 is illustrated within the parallel processor 200, any number of instances of the parallel processing unit 202 can be included. For example, multiple instances of the parallel processing unit 202 can be provided on a single add-in card, or multiple add-in cards can be interconnected. The different instances of the parallel processing unit 202 can be configured to inter-operate even if the different instances have different numbers of processing cores, different amounts of local parallel processor memory, and/or other configuration differences. For example, and in one embodiment, some instances of the parallel processing unit 202 can include higher precision floating point units relative to other instances. Systems incorporating one or more instances of the parallel processing unit 202 or the parallel processor 200 can be implemented in a variety of configurations and form factors, including but not limited to desktop, laptop, or handheld personal computers, servers, workstations, game consoles, and/or embedded systems.

FIG. 2B is a block diagram of a partition unit 220, according to an embodiment. In one embodiment, the partition unit 220 is an instance of one of the partition units 220A-220N of FIG. 2A. As illustrated, the partition unit 220 includes an L2 cache 221, a frame buffer interface 225, and a ROP 226 (raster operations unit). The L2 cache 221 is a read/write cache that is configured to perform load and store operations received from the memory crossbar 216 and ROP 226. Read misses and urgent write-back requests are output by L2 cache 221 to frame buffer interface 225 for processing. Dirty updates can also be sent to the frame buffer via the frame buffer interface 225 for opportunistic processing. In one embodiment, the frame buffer interface 225 interfaces with one of the memory units in parallel processor memory, such as the memory units 224A-224N of FIG. 2A (e.g., within parallel processor memory 222).

In graphics applications, the ROP 226 is a processing unit that performs raster operations, such as stencil, z test, blending, and the like. The ROP 226 then outputs processed graphics data that is stored in graphics memory. In some embodiments, the ROP 226 includes compression logic to compress z or color data that is written to memory and decompress z or color data that is read from memory. In some embodiments, the ROP 226 is included within each processing cluster (e.g., cluster 214A-214N of FIG. 2A) instead of within the partition unit 220. In such embodiment, read and write requests for pixel data are transmitted over the memory crossbar 216 instead of pixel fragment data.

The processed graphics data may be displayed on a display device, such as one of the one or more display device(s) 110 of FIG. 1, routed for further processing by the processor(s) 102, or routed for further processing by one of the processing entities within the parallel processor 200 of FIG. 2A.

FIG. 2C is a block diagram of a processing cluster 214 within a parallel processing unit, according to an embodiment. In one embodiment, the processing cluster is an instance of one of the processing clusters 214A-214N of FIG. 2A. The processing cluster 214 can be configured to execute many threads in parallel, where the term “thread” refers to an instance of a particular program executing on a particular set of input data. In some embodiments, single-instruction, multiple-data (SIMD) instruction issue techniques are used to support parallel execution of a large number of threads without providing multiple independent instruction units. In other embodiments, single-instruction, multiple-thread (SIMT) techniques are used to support parallel execution of a large number of generally synchronized threads, using a common instruction unit configured to issue instructions to a set of processing engines within each one of the processing clusters. Unlike a SIMD execution regime, where all processing engines typically execute identical instructions, SIMT execution allows different threads to more readily follow divergent execution paths through a given thread program. Persons skilled in the art will understand that a SIMD processing regime represents a functional subset of a SIMT processing regime.

Operation of the processing cluster 214 can be controlled via a pipeline manager 232 that distributes processing tasks to SIMT parallel processors. The pipeline manager 232 receives instructions from the scheduler 210 of FIG. 2A and manages execution of those instructions via a graphics multiprocessor 234 and/or a texture unit 236. The illustrated graphics multiprocessor 234 is an exemplary instance of an SIMT parallel processor. However, various types of SIMT parallel processors of differing architectures may be included within the processing cluster 214. One or more instances of the graphics multiprocessor 234 can be included within a processing cluster 214. The graphics multiprocessor 234 can process data and a data crossbar 240 can be used to distribute the processed data to one of multiple possible destinations, including other shader units. The pipeline manager 232 can facilitate the distribution of processed data by specifying destinations for processed data to be distributed vis the data crossbar 240.

Each graphics multiprocessor 234 within the processing cluster 214 can include an identical set of functional execution logic (e.g., arithmetic logic units, load-store units, etc.). The functional execution logic can be configured in a pipelined manner in which new instructions can be issued before previous instructions are complete. The functional execution logic may be provided. The functional logic supports a variety of operations including integer and floating point arithmetic comparison operations, Boolean operations bit-shifting, and computation of various algebraic functions. In one embodiment, the same functional-unit hardware can be leveraged to perform different operations and any combination of functional units may be present.

The instructions transmitted to the processing cluster 214 constitutes a thread. A set of threads executing across the set of parallel processing engines is a thread group. A thread group executes the same program on different input data. Each thread within a thread group can be assigned to a different processing engine within a graphics multiprocessor 234. A thread group may include fewer threads than the number of processing engines within the graphics multiprocessor 234. When a thread group includes fewer threads than the number of processing engines, one or more of the processing engines may be idle during cycles in which that thread group is being processed. A thread group may also include more threads than the number of processing engines within the graphics multiprocessor 234. When the thread group includes more threads than the number of processing engines within the graphics multiprocessor 234, processing can be performed over consecutive clock cycles. In one embodiment, multiple thread groups can be executed concurrently on a graphics multiprocessor 234.

In one embodiment, the graphics multiprocessor 234 includes an internal cache memory to perform load and store operations. In one embodiment, the graphics multiprocessor 234 can forego an internal cache and use a cache memory (e.g., L1 cache 308) within the processing cluster 214. Each graphics multiprocessor 234 also has access to L2 caches within the partition units (e.g., partition units 220A-220N of FIG. 2A) that are shared among all processing clusters 214 and may be used to transfer data between threads. The graphics multiprocessor 234 may also access off-chip global memory, which can include one or more of local parallel processor memory and/or system memory. Any memory external to the parallel processing unit 202 may be used as global memory. Embodiments in which the processing cluster 214 includes multiple instances of the graphics multiprocessor 234 can share common instructions and data, which may be stored in the L1 cache 308.

Each processing cluster 214 may include an MMU 245 (memory management unit) that is configured to map virtual addresses into physical addresses. In other embodiments, one or more instances of the MMU 245 may reside within the memory interface 218 of FIG. 2A. The MMU 245 includes a set of page table entries (PTEs) used to map a virtual address to a physical address of a tile (talk more about tiling) and optionally a cache line index. The MMU 245 may include address translation lookaside buffers (TLB) or caches that may reside within the graphics multiprocessor 234 or the L1 cache or processing cluster 214. The physical address is processed to distribute surface data access locality to allow efficient request interleaving among partition units. The cache line index may be used to determine whether a request for a cache line is a hit or miss.

In graphics and computing applications, a processing cluster 214 may be configured such that each graphics multiprocessor 234 is coupled to a texture unit 236 for performing texture mapping operations, e.g., determining texture sample positions, reading texture data, and filtering the texture data. Texture data is read from an internal texture L1 cache (not shown) or in some embodiments from the L1 cache within graphics multiprocessor 234 and is fetched from an L2 cache, local parallel processor memory, or system memory, as needed. Each graphics multiprocessor 234 outputs processed tasks to the data crossbar 240 to provide the processed task to another processing cluster 214 for further processing or to store the processed task in an L2 cache, local parallel processor memory, or system memory via the memory crossbar 216. A preROP 242 (pre-raster operations unit) is configured to receive data from graphics multiprocessor 234, direct data to ROP units, which may be located with partition units as described herein (e.g., partition units 220A-220N of FIG. 2A). The preROP 242 unit can perform optimizations for color blending, organize pixel color data, and perform address translations.

It will be appreciated that the core architecture described herein is illustrative and that variations and modifications are possible. Any number of processing units, e.g., graphics multiprocessor 234, texture units 236, preROPs 242, etc., may be included within a processing cluster 214. Further, while only one processing cluster 214 is shown, a parallel processing unit as described herein may include any number of instances of the processing cluster 214. In one embodiment, each processing cluster 214 can be configured to operate independently of other processing clusters 214 using separate and distinct processing units, L1 caches, etc.

FIG. 2D shows a graphics multiprocessor 234, according to one embodiment. In such embodiment, the graphics multiprocessor 234 couples with the pipeline manager 232 of the processing cluster 214. The graphics multiprocessor 234 has an execution pipeline including but not limited to an instruction cache 252, an instruction unit 254, an address mapping unit 256, a register file 258, one or more general purpose graphics processing unit (GPGPU) cores 262, and one or more load/store units 266. The GPGPU cores 262 and load/store units 266 are coupled with cache memory 272 and shared memory 270 via a memory and cache interconnect 268.

In one embodiment, the instruction cache 252 receives a stream of instructions to execute from the pipeline manager 232. The instructions are cached in the instruction cache 252 and dispatched for execution by the instruction unit 254. The instruction unit 254 can dispatch instructions as thread groups (e.g., warps), with each thread of the thread group assigned to a different execution unit within GPGPU core 262. An instruction can access any of a local, shared, or global address space by specifying an address within a unified address space. The address mapping unit 256 can be used to translate addresses in the unified address space into a distinct memory address that can be accessed by the load/store units 266.

The register file 258 provides a set of registers for the functional units of the graphics multiprocessor 324. The register file 258 provides temporary storage for operands connected to the data paths of the functional units (e.g., GPGPU cores 262, load/store units 266) of the graphics multiprocessor 324. In one embodiment, the register file 258 is divided between each of the functional units such that each functional unit is allocated a dedicated portion of the register file 258. In one embodiment, the register file 258 is divided between the different warps being executed by the graphics multiprocessor 324.

The GPGPU cores 262 can each include floating point units (FPUs) and/or integer arithmetic logic units (ALUs) that are used to execute instructions of the graphics multiprocessor 324. The GPGPU cores 262 can be similar in architecture or can differ in architecture, according to embodiments. For example, and in one embodiment, a first portion of the GPGPU cores 262 include a single precision FPU and an integer ALU while a second portion of the GPGPU cores include a double precision FPU. In one embodiment, the FPUs can implement the IEEE 754-2008 standard for floating point arithmetic or enable variable precision floating point arithmetic. The graphics multiprocessor 324 can additionally include one or more fixed function or special function units to perform specific functions such as copy rectangle or pixel blending operations. In one embodiment one or more of the GPGPU cores can also include fixed or special function logic.

The memory and cache interconnect 268 is an interconnect network that connects each of the functional units of the graphics multiprocessor 324 to the register file 258 and to the shared memory 270. In one embodiment, the memory and cache interconnect 268 is a crossbar interconnect that allows the load/store unit 266 to implement load and store operations between the shared memory 270 and the register file 258. The register file 258 can operate at the same frequency as the GPGPU cores 262, thus data transfer between the GPGPU cores 262 and the register file 258 is very low latency. The shared memory 270 can be used to enable communication between threads that execute on the functional units within the graphics multiprocessor 234. The cache memory 272 can be used as a data cache for example, to cache texture data communicated between the functional units and the texture unit 236. The shared memory 270 can also be used as a program managed cached. Threads executing on the GPGPU cores 262 can programmatically store data within the shared memory in addition to the automatically cached data that is stored within the cache memory 272.

FIGS. 3A-3B illustrate additional graphics multiprocessors, according to embodiments. The illustrated graphics multiprocessors 325, 350 are variants of the graphics multiprocessor 234 of FIG. 2C. The illustrated graphics multiprocessors 325, 350 can be configured as a streaming multiprocessor (SM) capable of simultaneous execution of a large number of execution threads.

FIG. 3A shows a graphics multiprocessor 325 according to an additional embodiment. The graphics multiprocessor 325 includes multiple additional instances of execution resource units relative to the graphics multiprocessor 234 of FIG. 2D. For example, the graphics multiprocessor 325 can include multiple instances of the instruction unit 332A-332B, register file 334A-334B, and texture unit(s) 344A-344B. The graphics multiprocessor 325 also includes multiple sets of graphics or compute execution units (e.g., GPGPU core 336A-336B, GPGPU core 337A-337B, GPGPU core 338A-338B) and multiple sets of load/store units 340A-340B. In one embodiment, the execution resource units have a common instruction cache 330, texture and/or data cache memory 342, and shared memory 346. The various components can communicate via an interconnect fabric 327. In one embodiment, the interconnect fabric 327 includes one or more crossbar switches to enable communication between the various components of the graphics multiprocessor 325.

FIG. 3B shows a graphics multiprocessor 350 according to an additional embodiment. The graphics processor includes multiple sets of execution resources 356A-356D, where each set of execution resource includes multiple instruction units, register files, GPGPU cores, and load store units, as illustrated in FIG. 2D and FIG. 3A. The execution resources 356A-356D can work in concert with texture unit(s) 360A-360D for texture operations, while sharing an instruction cache 354, and shared memory 362. In one embodiment, the execution resources 356A-356D can share an instruction cache 354 and shared memory 362, as well as multiple instances of a texture and/or data cache memory 358A-358B. The various components can communicate via an interconnect fabric 352 similar to the interconnect fabric 327 of FIG. 3A.

Persons skilled in the art will understand that the architecture described in FIGS. 1, 2A-2D, and 3A-3B are descriptive and not limiting as to the scope of the present embodiments. Thus, the techniques described herein may be implemented on any properly configured processing unit, including, without limitation, one or more mobile application processors, one or more desktop or server central processing units (CPUs) including multi-core CPUs, one or more parallel processing units, such as the parallel processing unit 202 of FIG. 2A, as well as one or more graphics processors or special purpose processing units, without departure from the scope of the embodiments described herein.

In some embodiments, a parallel processor or GPGPU as described herein is communicatively coupled to host/processor cores to accelerate graphics operations, machine-learning operations, pattern analysis operations, and various general purpose GPU (GPGPU) functions. The GPU may be communicatively coupled to the host processor/cores over a bus or other interconnect (e.g., a high-speed interconnect such as PCIe or NVLink). In other embodiments, the GPU may be integrated on the same package or chip as the cores and communicatively coupled to the cores over an internal processor bus/interconnect (i.e., internal to the package or chip). Regardless of the manner in which the GPU is connected, the processor cores may allocate work to the GPU in the form of sequences of commands/instructions contained in a work descriptor. The GPU then uses dedicated circuitry/logic for efficiently processing these commands/instructions.

Techniques for GPU to Host Processor Interconnection

FIG. 4A illustrates an exemplary architecture in which a plurality of GPUs 410-413 are communicatively coupled to a plurality of multi-core processors 405-406 over high-speed links 440-443 (e.g., buses, point-to-point interconnects, etc.). In one embodiment, the high-speed links 440-443 support a communication throughput of 4 GB/s, 30 GB/s, 80 GB/s or higher, depending on the implementation. Various interconnect protocols may be used including, but not limited to, PCIe 4.0 or 5.0 and NVLink 2.0. However, the underlying principles of the invention are not limited to any particular communication protocol or throughput.

In addition, in one embodiment, two or more of the GPUs 410-413 are interconnected over high-speed links 444-445, which may be implemented using the same or different protocols/links than those used for high-speed links 440-443. Similarly, two or more of the multi-core processors 405-406 may be connected over high speed link 433 which may be symmetric multi-processor (SMP) buses operating at 20 GB/s, 30 GB/s, 120 GB/s or higher. Alternatively, all communication between the various system components shown in FIG. 4A may be accomplished using the same protocols/links (e.g., over a common interconnection fabric). As mentioned, however, the underlying principles of the invention are not limited to any particular type of interconnect technology.

In one embodiment, each multi-core processor 405-406 is communicatively coupled to a processor memory 401-402, via memory interconnects 430-431, respectively, and each GPU 410-413 is communicatively coupled to GPU memory 420-423 over GPU memory interconnects 450-453, respectively. The memory interconnects 430-431 and 450-453 may utilize the same or different memory access technologies. By way of example, and not limitation, the processor memories 401-402 and GPU memories 420-423 may be volatile memories such as dynamic random access memories (DRAMs) (including stacked DRAMs), Graphics DDR SDRAM (GDDR) (e.g., GDDR5, GDDR6), or High Bandwidth Memory (HBM) and/or may be non-volatile memories such as 3D XPoint or Nano-Ram. In one embodiment, some portion of the memories may be volatile memory and another portion may be non-volatile memory (e.g., using a two-level memory (2LM) hierarchy).

As described below, although the various processors 405-406 and GPUs 410-413 may be physically coupled to a particular memory 401-402, 420-423, respectively, a unified memory architecture may be implemented in which the same virtual system address space (also referred to as the “effective address” space) is distributed among all of the various physical memories. For example, processor memories 401-402 may each comprise 64 GB of the system memory address space and GPU memories 420-423 may each comprise 32 GB of the system memory address space (resulting in a total of 256 GB addressable memory in this example).

FIG. 4B illustrates additional details for an interconnection between a multi-core processor 407 and a graphics acceleration module 446 in accordance with one embodiment. The graphics acceleration module 446 may include one or more GPU chips integrated on a line card which is coupled to the processor 407 via the high-speed link 440. Alternatively, the graphics acceleration module 446 may be integrated on the same package or chip as the processor 407.

The illustrated processor 407 includes a plurality of cores 460A-460D, each with a translation lookaside buffer 461A-461D and one or more caches 462A-462D. The cores may include various other components for executing instructions and processing data which are not illustrated to avoid obscuring the underlying principles of the invention (e.g., instruction fetch units, branch prediction units, decoders, execution units, reorder buffers, etc.). The caches 462A-462D may comprise level 1 (L1) and level 2 (L2) caches. In addition, one or more shared caches 426 may be included in the caching hierarchy and shared by sets of the cores 460A-460D. For example, one embodiment of the processor 407 includes 24 cores, each with its own L1 cache, twelve shared L2 caches, and twelve shared L3 caches. In this embodiment, one of the L2 and L3 caches are shared by two adjacent cores. The processor 407 and the graphics accelerator integration module 446 connect with system memory 441, which may include processor memories 401-402.

Coherency is maintained for data and instructions stored in the various caches 462A-462D, 456 and system memory 441 via inter-core communication over a coherence bus 464. For example, each cache may have cache coherency logic/circuitry associated therewith to communicate to over the coherence bus 464 in response to detected reads or writes to particular cache lines. In one implementation, a cache snooping protocol is implemented over the coherence bus 464 to snoop cache accesses. Cache snooping/coherency techniques are well understood by those of skill in the art and will not be described in detail here to avoid obscuring the underlying principles of the invention.

In one embodiment, a proxy circuit 425 communicatively couples the graphics acceleration module 446 to the coherence bus 464, allowing the graphics acceleration module 446 to participate in the cache coherence protocol as a peer of the cores. In particular, an interface 435 provides connectivity to the proxy circuit 425 over high-speed link 440 (e.g., a PCIe bus, NVLink, etc.) and an interface 437 connects the graphics acceleration module 446 to the link 440.

In one implementation, an accelerator integration circuit 436 provides cache management, memory access, context management, and interrupt management services on behalf of a plurality of graphics processing engines 431, 432, N of the graphics acceleration module 446. The graphics processing engines 431, 432, N may each comprise a separate graphics processing unit (GPU). Alternatively, the graphics processing engines 431, 432, N may comprise different types of graphics processing engines within a GPU such as graphics execution units, media processing engines (e.g., video encoders/decoders), samplers, and blit engines. In other words, the graphics acceleration module may be a GPU with a plurality of graphics processing engines 431-432, N or the graphics processing engines 431-432, N may be individual GPUs integrated on a common package, line card, or chip.

In one embodiment, the accelerator integration circuit 436 includes a memory management unit (MMU) 439 for performing various memory management functions such as virtual-to-physical memory translations (also referred to as effective-to-real memory translations) and memory access protocols for accessing system memory 441. The MMU 439 may also include a translation lookaside buffer (TLB) (not shown) for caching the virtual/effective to physical/real address translations. In one implementation, a cache 438 stores commands and data for efficient access by the graphics processing engines 431-432, N. In one embodiment, the data stored in cache 438 and graphics memories 433-434, N is kept coherent with the core caches 462A-462D, 456 and system memory 411. As mentioned, this may be accomplished via proxy circuit 425 which takes part in the cache coherency mechanism on behalf of cache 438 and memories 433-434, N (e.g., sending updates to the cache 438 related to modifications/accesses of cache lines on processor caches 462A-462D, 456 and receiving updates from the cache 438).

A set of registers 445 store context data for threads executed by the graphics processing engines 431-432, N and a context management circuit 448 manages the thread contexts. For example, the context management circuit 448 may perform save and restore operations to save and restore contexts of the various threads during contexts switches (e.g., where a first thread is saved and a second thread is stored so that the second thread can be execute by a graphics processing engine). For example, on a context switch, the context management circuit 448 may store current register values to a designated region in memory (e.g., identified by a context pointer). It may then restore the register values when returning to the context. In one embodiment, an interrupt management circuit 447 receives and processes interrupts received from system devices.

In one implementation, virtual/effective addresses from a graphics processing engine 431 are translated to real/physical addresses in system memory 411 by the MMU 439. One embodiment of the accelerator integration circuit 436 supports multiple (e.g., 4, 8, 16) graphics accelerator modules 446 and/or other accelerator devices. The graphics accelerator module 446 may be dedicated to a single application executed on the processor 407 or may be shared between multiple applications. In one embodiment, a virtualized graphics execution environment is presented in which the resources of the graphics processing engines 431-432, N are shared with multiple applications or virtual machines (VMs). The resources may be subdivided into “slices” which are allocated to different VMs and/or applications based on the processing requirements and priorities associated with the VMs and/or applications.

Thus, the accelerator integration circuit acts as a bridge to the system for the graphics acceleration module 446 and provides address translation and system memory cache services. In addition, the accelerator integration circuit 436 may provide virtualization facilities for the host processor to manage virtualization of the graphics processing engines, interrupts, and memory management.

Because hardware resources of the graphics processing engines 431-432, N are mapped explicitly to the real address space seen by the host processor 407, any host processor can address these resources directly using an effective address value. One function of the accelerator integration circuit 436, in one embodiment, is the physical separation of the graphics processing engines 431-432, N so that they appear to the system as independent units.

As mentioned, in the illustrated embodiment, one or more graphics memories 433-434, M are coupled to each of the graphics processing engines 431-432, N, respectively. The graphics memories 433-434, M store instructions and data being processed by each of the graphics processing engines 431-432, N. The graphics memories 433-434, M may be volatile memories such as DRAMs (including stacked DRAMs), GDDR memory (e.g., GDDR5, GDDR6), or HBM, and/or may be non-volatile memories such as 3D XPoint or Nano-Ram.

In one embodiment, to reduce data traffic over link 440, biasing techniques are used to ensure that the data stored in graphics memories 433-434, M is data which will be used most frequently by the graphics processing engines 431-432, N and preferably not used by the cores 460A-460D (at least not frequently). Similarly, the biasing mechanism attempts to keep data needed by the cores (and preferably not the graphics processing engines 431-432, N) within the caches 462A-462D, 456 of the cores and system memory 411.

FIG. 4C illustrates another embodiment in which the accelerator integration circuit 436 is integrated within the processor 407. In this embodiment, the graphics processing engines 431-432, N communicate directly over the high-speed link 440 to the accelerator integration circuit 436 via interface 437 and interface 435 (which, again, may be utilize any form of bus or interface protocol). The accelerator integration circuit 436 may perform the same operations as those described with respect to FIG. 4B, but potentially at a higher throughput given its close proximity to the coherency bus 462 and caches 462A-462D, 426.

One embodiment supports different programming models including a dedicated-process programming model (no graphics acceleration module virtualization) and shared programming models (with virtualization). The latter may include programming models which are controlled by the accelerator integration circuit 436 and programming models which are controlled by the graphics acceleration module 446.

In one embodiment of the dedicated process model, graphics processing engines 431-432, N are dedicated to a single application or process under a single operating system. The single application can funnel other application requests to the graphics engines 431-432, N, providing virtualization within a VM/partition.

In the dedicated-process programming models, the graphics processing engines 431-432, N, may be shared by multiple VM/application partitions. The shared models require a system hypervisor to virtualize the graphics processing engines 431-432, N to allow access by each operating system. For single-partition systems without a hypervisor, the graphics processing engines 431-432, N are owned by the operating system. In both cases, the operating system can virtualize the graphics processing engines 431-432, N to provide access to each process or application.

For the shared programming model, the graphics acceleration module 446 or an individual graphics processing engine 431-432, N selects a process element using a process handle. In one embodiment, process elements are stored in system memory 411 and are addressable using the effective address to real address translation techniques described herein. The process handle may be an implementation-specific value provided to the host process when registering its context with the graphics processing engine 431-432, N (that is, calling system software to add the process element to the process element linked list). The lower 16-bits of the process handle may be the offset of the process element within the process element linked list.

FIG. 4D illustrates an exemplary accelerator integration slice 490. As used herein, a “slice” comprises a specified portion of the processing resources of the accelerator integration circuit 436. Application effective address space 482 within system memory 411 stores process elements 483. In one embodiment, the process elements 483 are stored in response to GPU invocations 481 from applications 480 executed on the processor 407. A process element 483 contains the process state for the corresponding application 480. A work descriptor (WD) 484 contained in the process element 483 can be a single job requested by an application or may contain a pointer to a queue of jobs. In the latter case, the WD 484 is a pointer to the job request queue in the application's address space 482.

The graphics acceleration module 446 and/or the individual graphics processing engines 431-432, N can be shared by all or a subset of the processes in the system. Embodiments of the invention include an infrastructure for setting up the process state and sending a WD 484 to a graphics acceleration module 446 to start a job in a virtualized environment.

In one implementation, the dedicated-process programming model is implementation-specific. In this model, a single process owns the graphics acceleration module 446 or an individual graphics processing engine 431. Because the graphics acceleration module 446 is owned by a single process, the hypervisor initializes the accelerator integration circuit 436 for the owning partition and the operating system initializes the accelerator integration circuit 436 for the owning process at the time when the graphics acceleration module 446 is assigned.

In operation, a WD fetch unit 491 in the accelerator integration slice 490 fetches the next WD 484 which includes an indication of the work to be done by one of the graphics processing engines of the graphics acceleration module 446. Data from the WD 484 may be stored in registers 445 and used by the MMU 439, interrupt management circuit 447 and/or context management circuit 446 as illustrated. For example, one embodiment of the MMU 439 includes segment/page walk circuitry for accessing segment/page tables 486 within the OS virtual address space 485. The interrupt management circuit 447 may process interrupt events 492 received from the graphics acceleration module 446. When performing graphics operations, an effective address 493 generated by a graphics processing engine 431-432, N is translated to a real address by the MMU 439.

In one embodiment, the same set of registers 445 are duplicated for each graphics processing engine 431-432, N and/or graphics acceleration module 446 and may be initialized by the hypervisor or operating system. Each of these duplicated registers may be included in an accelerator integration slice 490. Exemplary registers that may be initialized by the hypervisor are shown in Table 1.

TABLE 1 Hypervisor Initialized Registers 1 Slice Control Register 2 Real Address (RA) Scheduled Processes Area Pointer 3 Authority Mask Override Register 4 Interrupt Vector Table Entry Offset 5 Interrupt Vector Table Entry Limit 6 State Register 7 Logical Partition ID 8 Real address (RA) Hypervisor Accelerator Utilization Record Pointer 9 Storage Description Register

Exemplary registers that may be initialized by the operating system are shown in Table 2.

TABLE 2 Operating System Initialized Registers 1 Process and Thread Identification 2 Effective Address (EA) Context Save/Restore Pointer 3 Virtual Address (VA) Accelerator Utilization Record Pointer 4 Virtual Address (VA) Storage Segment Table Pointer 5 Authority Mask 6 Work descriptor

In one embodiment, each WD 484 is specific to a particular graphics acceleration module 446 and/or graphics processing engines 431-432, N. It contains all the information a graphics processing engine 431-432, N requires to do its work or it can be a pointer to a memory location where the application has set up a command queue of work to be completed.

FIG. 4E illustrates additional details for one embodiment of a shared model. This embodiment includes a hypervisor real address space 498 in which a process element list 499 is stored. The hypervisor real address space 498 is accessible via a hypervisor 496 which virtualizes the graphics acceleration module engines for the operating system 495.

The shared programming models allow for all or a subset of processes from all or a subset of partitions in the system to use a graphics acceleration module 446. There are two programming models where the graphics acceleration module 446 is shared by multiple processes and partitions: time-sliced shared and graphics directed shared.

In this model, the system hypervisor 496 owns the graphics acceleration module 446 and makes its function available to all operating systems 495. For a graphics acceleration module 446 to support virtualization by the system hypervisor 496, the graphics acceleration module 446 may adhere to the following requirements: 1) An application's job request must be autonomous (that is, the state does not need to be maintained between jobs), or the graphics acceleration module 446 must provide a context save and restore mechanism. 2) An application's job request is guaranteed by the graphics acceleration module 446 to complete in a specified amount of time, including any translation faults, or the graphics acceleration module 446 provides the ability to preempt the processing of the job. 3) The graphics acceleration module 446 must be guaranteed fairness between processes when operating in the directed shared programming model.

In one embodiment, for the shared model, the application 480 is required to make an operating system 495 system call with a graphics acceleration module 446 type, a work descriptor (WD), an authority mask register (AMR) value, and a context save/restore area pointer (CSRP). The graphics acceleration module 446 type describes the targeted acceleration function for the system call. The graphics acceleration module 446 type may be a system-specific value. The WD is formatted specifically for the graphics acceleration module 446 and can be in the form of a graphics acceleration module 446 command, an effective address pointer to a user-defined structure, an effective address pointer to a queue of commands, or any other data structure to describe the work to be done by the graphics acceleration module 446. In one embodiment, the AMR value is the AMR state to use for the current process. The value passed to the operating system is similar to an application setting the AMR. If the accelerator integration circuit 436 and graphics acceleration module 446 implementations do not support a User Authority Mask Override Register (UAMOR), the operating system may apply the current UAMOR value to the AMR value before passing the AMR in the hypervisor call. The hypervisor 496 may optionally apply the current Authority Mask Override Register (AMOR) value before placing the AMR into the process element 483. In one embodiment, the CSRP is one of the registers 445 containing the effective address of an area in the application's address space 482 for the graphics acceleration module 446 to save and restore the context state. This pointer is optional if no state is required to be saved between jobs or when a job is preempted. The context save/restore area may be pinned system memory.

Upon receiving the system call, the operating system 495 may verify that the application 480 has registered and been given the authority to use the graphics acceleration module 446. The operating system 495 then calls the hypervisor 496 with the information shown in Table 3.

TABLE 3 OS to Hypervisor Call Parameters 1 A work descriptor (WD) 2 An Authority Mask Register (AMR) value (potentially masked). 3 An effective address (EA) Context Save/Restore Area Pointer (CSRP) 4 A process ID (PID) and optional thread ID (TID) 5 A virtual address (VA) accelerator utilization record pointer (AURP) 6 The virtual address of the storage segment table pointer (SSTP) 7 A logical interrupt service number (LISN)

Upon receiving the hypervisor call, the hypervisor 496 verifies that the operating system 495 has registered and been given the authority to use the graphics acceleration module 446. The hypervisor 496 then puts the process element 483 into the process element linked list for the corresponding graphics acceleration module 446 type. The process element may include the information shown in Table 4

TABLE 4 Process Element Information 1 A work descriptor (WD) 2 An Authority Mask Register (AMR) value (potentially masked). 3 An effective address (EA) Context Save/Restore Area Pointer (CSRP) 4 A process ID (PID) and optional thread ID (TID) 5 A virtual address (VA) accelerator utilization record pointer (AURP) 6 The virtual address of the storage segment table pointer (SSTP) 7 A logical interrupt service number (LISN) 8 Interrupt vector table, derived from the hypervisor call parameters. 9 A state register (SR) value 10 A logical partition ID (LPID) 11 A real address (RA) hypervisor accelerator utilization record pointer 12 The Storage Descriptor Register (SDR)

In one embodiment, the hypervisor initializes a plurality of accelerator integration slice 490 registers 445.

As illustrated in FIG. 4F, one embodiment of the invention employs a unified memory addressable via a common virtual memory address space used to access the physical processor memories 401-402 and GPU memories 420-423. In this implementation, operations executed on the GPUs 410-413 utilize the same virtual/effective memory address space to access the processors memories 401-402 and vice versa, thereby simplifying programmability. In one embodiment, a first portion of the virtual/effective address space is allocated to the processor memory 401, a second portion to the second processor memory 402, a third portion to the GPU memory 420, and so on. The entire virtual/effective memory space (sometimes referred to as the effective address space) is thereby distributed across each of the processor memories 401-402 and GPU memories 420-423, allowing any processor or GPU to access any physical memory with a virtual address mapped to that memory.

In one embodiment, bias/coherence management circuitry 494A-494E within one or more of the MMUs 439A-439E ensures cache coherence between the caches of the host processors (e.g., 405) and the GPUs 410-413 and implements biasing techniques indicating the physical memories in which certain types of data should be stored. While multiple instances of bias/coherence management circuitry 494A-494E are illustrated in FIG. 4F, the bias/coherence circuitry may be implemented within the MMU of one or more host processors 405 and/or within the accelerator integration circuit 436.

One embodiment allows GPU-attached memory 420-423 to be mapped as part of system memory, and accessed using shared virtual memory (SVM) technology, but without suffering the typical performance drawbacks associated with full system cache coherence. The ability to GPU-attached memory 420-423 to be accessed as system memory without onerous cache coherence overhead provides a beneficial operating environment for GPU offload. This arrangement allows the host processor 405 software to setup operands and access computation results, without the overhead of tradition I/O DMA data copies. Such traditional copies involve driver calls, interrupts and memory mapped I/O (MMIO) accesses that are all inefficient relative to simple memory accesses. At the same time, the ability to access GPU attached memory 420-423 without cache coherence overheads can be critical to the execution time of an offloaded computation. In cases with substantial streaming write memory traffic, for example, cache coherence overhead can significantly reduce the effective write bandwidth seen by a GPU 410-413. The efficiency of operand setup, the efficiency of results access, and the efficiency of GPU computation all play a role in determining the effectiveness of GPU offload.

In one implementation, the selection of between GPU bias and host processor bias is driven by a bias tracker data structure. A bias table may be used, for example, which may be a page-granular structure (i.e., controlled at the granularity of a memory page) that includes 1 or 2 bits per GPU-attached memory page. The bias table may be implemented in a stolen memory range of one or more GPU-attached memories 420-423, with or without a bias cache in the GPU 410-413 (e.g., to cache frequently/recently used entries of the bias table). Alternatively, the entire bias table may be maintained within the GPU.

In one implementation, the bias table entry associated with each access to the GPU-attached memory 420-423 is accessed prior the actual access to the GPU memory, causing the following operations. First, local requests from the GPU 410-413 that find their page in GPU bias are forwarded directly to a corresponding GPU memory 420-423. Local requests from the GPU that find their page in host bias are forwarded to the processor 405 (e.g., over a high-speed link as discussed above). In one embodiment, requests from the processor 405 that find the requested page in host processor bias complete the request like a normal memory read. Alternatively, requests directed to a GPU-biased page may be forwarded to the GPU 410-413. The GPU may then transition the page to a host processor bias if it is not currently using the page.

The bias state of a page can be changed either by a software-based mechanism, a hardware-assisted software-based mechanism, or, for a limited set of cases, a purely hardware-based mechanism.

One mechanism for changing the bias state employs an API call (e.g. OpenCL), which, in turn, calls the GPU's device driver which, in turn, sends a message (or enqueues a command descriptor) to the GPU directing it to change the bias state and, for some transitions, perform a cache flushing operation in the host. The cache flushing operation is required for a transition from host processor 405 bias to GPU bias, but is not required for the opposite transition.

In one embodiment, cache coherency is maintained by temporarily rendering GPU-biased pages uncacheable by the host processor 405. To access these pages, the processor 405 may request access from the GPU 410 which may or may not grant access right away, depending on the implementation. Thus, to reduce communication between the processor 405 and GPU 410 it is beneficial to ensure that GPU-biased pages are those which are required by the GPU but not the host processor 405 and vice versa.

Graphics Processing Pipeline

FIG. 5 illustrates a graphics processing pipeline 500, according to an embodiment. In one embodiment, a graphics processor can implement the illustrated graphics processing pipeline 500. The graphics processor can be included within the parallel processing subsystems as described herein, such as the parallel processor 200 of FIG. 2A, which, in one embodiment, is a variant of the parallel processor(s) 112 of FIG. 1. The various parallel processing systems can implement the graphics processing pipeline 500 via one or more instances of the parallel processing unit (e.g., parallel processing unit 202 of FIG. 2A) as described herein. For example, a shader unit (e.g., graphics multiprocessor 234 of FIG. 2D) may be configured to perform the functions of one or more of a vertex processing unit 504, a tessellation control processing unit 508, a tessellation evaluation processing unit 512, a geometry processing unit 516, and a fragment/pixel processing unit 524. The functions of data assembler 502, primitive assemblers 506, 514, 518, tessellation unit 510, rasterizer 522, and raster operations unit 526 may also be performed by other processing engines within a processing cluster (e.g., processing cluster 214 of FIG. 3A) and a corresponding partition unit (e.g., partition unit 220A-220N of FIG. 2C). The graphics processing pipeline 500 may also be implemented using dedicated processing units for one or more functions. In one embodiment, one or more portions of the graphics processing pipeline 500 can be performed by parallel processing logic within a general-purpose processor (e.g., CPU). In one embodiment, one or more portions of the graphics processing pipeline 500 can access on-chip memory (e.g., parallel processor memory 222 as in FIG. 2A) via a memory interface 528, which may be an instance of the memory interface 218 of FIG. 2A.

In one embodiment, the data assembler 502 is a processing unit that collects vertex data for surfaces and primitives. The data assembler 502 then outputs the vertex data, including the vertex attributes, to the vertex processing unit 504. The vertex processing unit 504 is a programmable execution unit that executes vertex shader programs, lighting and transforming vertex data as specified by the vertex shader programs. The vertex processing unit 504 reads data that is stored in cache, local or system memory for use in processing the vertex data and may be programmed to transform the vertex data from an object-based coordinate representation to a world space coordinate space or a normalized device coordinates space.

A first instance of a primitive assembler 506 receives vertex attributes from the vertex processing unit 504. The primitive assembler 506 readings stored vertex attributes as needed and constructs graphics primitives for processing by tessellation control processing unit 508. The graphics primitives include triangles, line segments, points, patches, and so forth, as supported by various graphics processing application programming interfaces (APIs).

The tessellation control processing unit 508 treats the input vertices as control points for a geometric patch. The control points are transformed from an input representation from the patch (e.g., the patch's bases) to a representation that is suitable for use in surface evaluation by the tessellation evaluation processing unit 512. The tessellation control processing unit 508 can also compute tessellation factors for edges of geometric patches. A tessellation factor applies to a single edge and quantifies a view-dependent level of detail associated with the edge. A tessellation unit 510 is configured to receive the tessellation factors for edges of a patch and to tessellate the patch into multiple geometric primitives such as line, triangle, or quadrilateral primitives, which are transmitted to a tessellation evaluation processing unit 512. The tessellation evaluation processing unit 512 operates on parameterized coordinates of the subdivided patch to generate a surface representation and vertex attributes for each vertex associated with the geometric primitives.

A second instance of a primitive assembler 514 receives vertex attributes from the tessellation evaluation processing unit 512, reading stored vertex attributes as needed, and constructs graphics primitives for processing by the geometry processing unit 516. The geometry processing unit 516 is a programmable execution unit that executes geometry shader programs to transform graphics primitives received from primitive assembler 514 as specified by the geometry shader programs. In one embodiment, the geometry processing unit 516 is programmed to subdivide the graphics primitives into one or more new graphics primitives and calculate parameters used to rasterize the new graphics primitives.

In some embodiments, the geometry processing unit 516 can add or delete elements in the geometry stream. The geometry processing unit 516 outputs the parameters and vertices specifying new graphics primitives to primitive assembler 518. The primitive assembler 518 receives the parameters and vertices from the geometry processing unit 516 and constructs graphics primitives for processing by a viewport scale, cull, and clip unit 520. The geometry processing unit 516 reads data that is stored in parallel processor memory or system memory for use in processing the geometry data. The viewport scale, cull, and clip unit 520 performs clipping, culling, and viewport scaling and outputs processed graphics primitives to a rasterizer 522.

The rasterizer 522 can perform depth culling and other depth-based optimizations. The rasterizer 522 also performs scan conversion on the new graphics primitives to generate fragments and outputs those fragments and associated coverage data to the fragment/pixel processing unit 524.

The fragment/pixel processing unit 524 is a programmable execution unit that is configured to execute fragment shader programs or pixel shader programs. The fragment/pixel processing unit 524 transforming fragments or pixels received from rasterizer 522, as specified by the fragment or pixel shader programs. For example, the fragment/pixel processing unit 524 may be programmed to perform operations included but not limited to texture mapping, shading, blending, texture correction and perspective correction to produce shaded fragments or pixels that are output to a raster operations unit 526. The fragment/pixel processing unit 524 can read data that is stored in either the parallel processor memory or the system memory for use when processing the fragment data. Fragment or pixel shader programs may be configured to shade at sample, pixel, tile, or other granularities, depending on the sampling rate configured for the processing units.

The raster operations unit 526 is a processing unit that performs raster operations including, but not limited to stencil, z test, blending, and the like, and outputs pixel data as processed graphics data to be storage in graphics memory, e.g., parallel processor memory 222 as in FIG. 2A, and/or system memory 104 as in FIG. 1, to be displayed on the one or more display device(s) 110 or for further processing by one of the one or more processor(s) 102 or parallel processor(s) 112. In some embodiments, the raster operations unit 526 is configured to compress z or color data that is written to memory and decompress z or color data that is read from memory.

FIG. 6 illustrates a computing device 600 hosting a de-centralized load-balancing mechanism (“de-centralized mechanism”) 610 according to one embodiment. Computing device 600 represents a communication and data processing device including (but not limited to) smart wearable devices, smartphones, virtual reality (VR) devices, head-mounted display (HMDs), mobile computers, Internet of Things (IoT) devices, laptop computers, desktop computers, server computers, etc., and be similar to or the same as computing device 100 of FIG. 1; accordingly, for brevity, clarity, and ease of understanding, many of the details stated above with reference to FIGS. 1-5 are not further discussed or repeated hereafter.

Computing device 600 may further include (without limitations) an autonomous machine or an artificially intelligent agent, such as a mechanical agent or machine, an electronics agent or machine, a virtual agent or machine, an electro-mechanical agent or machine, etc. Examples of autonomous machines or artificially intelligent agents may include (without limitation) robots, autonomous vehicles (e.g., self-driving cars, self-flying planes, self-sailing boats, etc.), autonomous equipment (self-operating construction vehicles, self-operating medical equipment, etc.), and/or the like. Throughout this document, “computing device” may be interchangeably referred to as “autonomous machine” or “artificially intelligent agent” or simply “robot”.

Computing device 600 may further include (without limitations) large computing systems, such as server computers, desktop computers, etc., and may further include set-top boxes (e.g., Internet-based cable television set-top boxes, etc.), global positioning system (GPS)-based devices, etc. Computing device 600 may include mobile computing devices serving as communication devices, such as cellular phones including smartphones, personal digital assistants (PDAs), tablet computers, laptop computers, e-readers, smart televisions, television platforms, wearable devices (e.g., glasses, watches, bracelets, smartcards, jewelry, clothing items, etc.), media players, etc. For example, in one embodiment, computing device 600 may include a mobile computing device employing a computer platform hosting an integrated circuit (“IC”), such as system on a chip (“SoC” or “SOC”), integrating various hardware and/or software components of computing device 600 on a single chip.

As illustrated, in one embodiment, computing device 600 may include any number and type of hardware and/or software components, such as (without limitation) graphics processing unit (“GPU” or simply “graphics processor”) 614, graphics driver (also referred to as “GPU driver”, “graphics driver logic”, “driver logic”, user-mode driver (UMD), UMD, user-mode driver framework (UMDF), UMDF, or simply “driver”) 616, central processing unit (“CPU” or simply “application processor”) 612, memory 608, network devices, drivers, or the like, as well as input/output (I/O) sources 604, such as touchscreens, touch panels, touch pads, virtual or regular keyboards, virtual or regular mice, ports, connectors, etc. Computing device 600 may include operating system (OS) 606 serving as an interface between hardware and/or physical resources of the computer device 600 and a user. It is contemplated that graphics processor 614 and application processor 612 may be one or more of processor(s) 102 of FIG. 1 and/or processor(s) 1002 of FIG. 10.

It is to be appreciated that a lesser or more equipped system than the example described above may be preferred for certain implementations. Therefore, the configuration of computing device 600 may vary from implementation to implementation depending upon numerous factors, such as price constraints, performance requirements, technological improvements, or other circumstances.

Embodiments may be implemented as any or a combination of: one or more microchips or integrated circuits interconnected using a parentboard, hardwired logic, software stored by a memory device and executed by a microprocessor, firmware, an application specific integrated circuit (ASIC), and/or a field programmable gate array (FPGA). The terms “logic”, “module”, “component”, “engine”, and “mechanism” may include, by way of example, software or hardware and/or combinations of software and hardware.

In one embodiment, de-centralized mechanism 610 may be hosted or facilitated by operating system 606 of computing device 600. In another embodiment, de-centralized mechanism 610 may be hosted by or part of graphics processing unit (“GPU” or simply “graphics processor”) 614 or firmware of graphics processor 614. Similarly, in yet another embodiment, de-centralized mechanism 610 may be hosted by or part of a microcontroller. In yet another embodiment, de-centralized mechanism 610 may be hosted by or part of any number and type of components of computing device 600, such as a portion of de-centralized mechanism 610 may be hosted by or part of operating system 606, another portion may be hosted by or part of graphics processor 614, another portion may be hosted by or part of a microcontroller, while one or more portions of de-centralized mechanism 610 may be hosted by or part of any number and type of other devices of computing device 600. It is contemplated that one or more portions or components of de-centralized mechanism 610 may be employed as hardware, software, and/or firmware.

In the illustrated embodiment, de-centralized mechanism 610 is shown as being hosted by or part of graphics processor 614 but that embodiments are not limited as such. It is contemplated that embodiments are not limited to any particular implementation or hosting of de-centralized mechanism 610 and that de-centralized mechanism 610 and one or more of its components may be implemented as hardware, software, firmware, or any combination thereof.

Computing device 600 may host network interface(s) to provide access to a network, such as a LAN, a wide area network (WAN), a metropolitan area network (MAN), a personal area network (PAN), Bluetooth, a cloud network, a mobile network (e.g., 3^(rd) Generation (3G), 4^(th) Generation (4G), etc.), an intranet, the Internet, etc. Network interface(s) may include, for example, a wireless network interface having antenna, which may represent one or more antenna(e). Network interface(s) may also include, for example, a wired network interface to communicate with remote devices via network cable, which may be, for example, an Ethernet cable, a coaxial cable, a fiber optic cable, a serial cable, or a parallel cable.

Embodiments may be provided, for example, as a computer program product which may include one or more machine-readable media having stored thereon machine-executable instructions that, when executed by one or more machines such as a computer, network of computers, or other electronic devices, may result in the one or more machines carrying out operations in accordance with embodiments described herein. A machine-readable medium may include, but is not limited to, floppy diskettes, optical disks, CD-ROMs (Compact Disc-Read Only Memories), and magneto-optical disks, ROMs, RAMs, EPROMs (Erasable Programmable Read Only Memories), EEPROMs (Electrically Erasable Programmable Read Only Memories), magnetic or optical cards, flash memory, or other type of media/machine-readable medium suitable for storing machine-executable instructions.

Moreover, embodiments may be downloaded as a computer program product, wherein the program may be transferred from a remote computer (e.g., a server) to a requesting computer (e.g., a client) by way of one or more data signals embodied in and/or modulated by a carrier wave or other propagation medium via a communication link (e.g., a modem and/or network connection).

Throughout the document, term “user” may be interchangeably referred to as “viewer”, “observer”, “person”, “individual”, “end-user”, and/or the like. It is to be noted that throughout this document, terms like “graphics domain” may be referenced interchangeably with “graphics processing unit”, “graphics processor”, or simply “GPU” and similarly, “CPU domain” or “host domain” may be referenced interchangeably with “computer processing unit”, “application processor”, or simply “CPU”.

It is to be noted that terms like “node”, “computing node”, “server”, “server device”, “cloud computer”, “cloud server”, “cloud server computer”, “machine”, “host machine”, “device”, “computing device”, “computer”, “computing system”, and the like, may be used interchangeably throughout this document. It is to be further noted that terms like “application”, “software application”, “program”, “software program”, “package”, “software package”, and the like, may be used interchangeably throughout this document. Also, terms like “job”, “input”, “request”, “message”, and the like, may be used interchangeably throughout this document.

FIG. 7 illustrates de-centralized mechanism 610 of FIG. 6 according to one embodiment. For brevity, many of the details already discussed with reference to FIGS. 1-6 are not repeated or discussed hereafter. In one embodiment, de-centralized mechanism 610 may include any number and type of components, such as (without limitations): detection/migration logic 701; copy engine reference logic 703; scheduling/loading logic 705; and communication/compatibility logic 707.

As aforementioned, a global resource, such as a parser, may become bottlenecked in distributing tasks smaller than a runlist level to distribute multiple slices or SMs or SMMs to keep them occupied to get the best performance for GPGPU tasks. Typically, for those dispatches that are based on thread groups, several thread groups from multiple task queues or contexts can have different shader complexity. This results in load-balancing issues which cannot be addressed by using the conventional centralized scheduler. Once dispatch to a slice or SM, conventional techniques do not provide for load balance dynamically, any later.

Embodiments provide for a novel technique, as facilitated by de-centralized mechanism 610, to allow for threads or thread groups to move or migrate from one slice or SM or SMM or even graphics processor 614 to another slice or SM or SMM (throughout this document, “slice”, “SM”, and “SMM” are referenced interchangeably or, for brevity, simply as “slice” or “slice/SM/SMM”) or graphics processor, respectively, using copy engines both dynamically and without involving the conventional centralized scheduler.

In one embodiment, as illustrated with reference to FIG. 9, de-centralized mechanism 610 being hosted by graphics processor 614 can be used as a local scheduling system, where each slice of graphics processor 614 (and/or graphics processor 614 itself) is capable of monitoring compute utilization relating to execution units (EUs), such as running threads, scheduled threads, etc., as facilitated by detection/migration logic 701. Further, using communication/compatibility logic 707, this monitored or detected utilization may be communicated to neighboring slices and/or graphics processors using a communication link, such as using a simple protocol on an existing physical link. This protocol may then be further facilitated by communication/compatibility logic 707 to periodically or continuously or on-demand communicate with neighboring slices and/or graphics processors regarding the utilization and thus, based on the topology of connection, each slice and/or graphics processor 614 gets a view of its neighbor's utilization.

As further facilitated by detection/migration logic 701, each slice is capable of evaluating its neighbors and own load and accordingly, decides whether to move or migrate any of the threads, thread groups, and/or tasks (for brevity, collectively referred to as “threads”) to the neighboring threads, thread groups, and/or tasks, respectively, by attempting to reserve the empty thread slots. Since there might be a sense of race or rush of threads to multiple neighbors, a tie-breaker may be employed and used in cases when multiple threads are tied for the spot, such that a time may be broken based on the age of the threads wanting to migrate or the task-level execution priority set by the command sequencer at a global level or, in some embodiments, the application-level priority as determined by the operating system, such as operating system 606 of FIG. 6.

Once a reservation succeeds, based on the above criteria and tie-breaker, copy engine may be enabled to start copying the thread context as facilitated by a copy engine reference logic 703. For example, any necessary state and data for threads (such as those that are yet to start or paused or stalled) are mapped using a memory mapped address space, such as src and dest address spaces are known and a fixed set of data is needed to be migrated depending on the stalled versus yet to launch threads. For example, using copy engine reference logic 703, a copy engine may at the src of the link may gather address-space for src and start streaming data to the destination address space, where this allows for a rapid movement of any thread state context.

In one embodiment, a final synchronization of completion of the threads is communicated on to the originating slice and/or graphics processor 614 as facilitated by communication/compatibility logic 707. By allowing this migration of threads among neighbors, power and latencies have tighter upper bounds and constant overhead for the most part, as opposed to routing to the farthest slice and/or graphics processor that is least loaded as determined and executed by scheduling/loading logic 705. In one embodiment, once the necessary monitoring and migrations are made by detection/migration logic 701 and copy engine tasks are performed as copy engine reference logic 703 and communications are facilitated by communication/compatibility logic 707, scheduling/loading logic 705 may then serve as a local scheduler to ensure the application of the criteria and execution of load balancing that is fast, accurate, efficient, and local.

Moreover, conventional thread group scheduling techniques use an algorithm that tries to load-balance hardware resources at schedule time; however, as some threads or thread groups finish early, the system typically goes out-of-balance over time. This issue often leads to fragmentation of shared local memory (SLM) space and hardware barriers, which can prevent scheduling of new thread groups if none of a slice has sufficient SLM space due to fragmentation.

Embodiments further provide for a novel technique, as facilitated by de-centralized mechanism 610, for dynamic load-balancing of dual sub-slice (DSS) or SMs/SMMs, where any of the threads or thread groups may be migrated from one slice to another and so on and/or the SLM space of a running thread group may be moved from one location to another (such as within the same slice) to achieve better load-balancing and de-fragmentation of SLM.

In one embodiment, detection/migration logic 701 of de-centralized mechanism 610 may periodically monitor or check the load-balancing and SLM space fragmentation of all slices of graphics processor 614. If, for example, detection/migration logic 701 detects that load-balancing is unbalanced or out-of-balance, such as due to some threads or thread groups being completed, detection/migration logic 701 may then migrate those threads or thread groups from one slice another slice.

Subsequently, in one embodiment, scheduling/loading logic 705 may scheduling a new thread or thread group to take over if detection/migration logic 701 detects that it may not schedule the thread/thread group due to fragmentation of the SLM, such as due to there being no single slice with sufficient SLM space to take over. In this case, detection/migration logic 701 may migrate the thread/thread groups from one slice to another slice to effectively de-fragment the SLM space and create enough free space so that a new thread/thread group may be scheduled to take over. This novel technique allows for ensuring balancing of load (such as graphics processor load) across all hardware resources more uniformly, while yielding better performance efficiency.

Communication/compatibility logic 707 may be used to facilitate dynamic communication and compatibility between computing device 600 and any number and type of other computing devices (such as mobile computing device, desktop computer, server computing device, etc.); processing devices or components (such as CPUs, GPUs, etc.); capturing/sensing/detecting devices (such as capturing/sensing components including cameras, depth sensing cameras, camera sensors, red green blue (“RGB” or “rgb”) sensors, microphones, etc.); display devices (such as output components including display screens, display areas, display projectors, etc.); user/context-awareness components and/or identification/verification sensors/devices (such as biometric sensors/detectors, scanners, etc.); database(s) 730, such as memory or storage devices, databases, and/or data sources (such as data storage devices, hard drives, solid-state drives, hard disks, memory cards or devices, memory circuits, etc.); communication medium(s) 725, such as one or more communication channels or networks (e.g., cloud networks, the Internet, intranets, cellular networks, proximity networks, such as Bluetooth, Bluetooth low energy (BLE), Bluetooth Smart, Wi-Fi proximity, Radio Frequency Identification (RFID), Near Field Communication (NFC), Body Area Network (BAN), etc.); wireless or wired communications and relevant protocols (e.g., Wi-Fi®, WiMAX, Ethernet, etc.); connectivity and location management techniques; software applications/websites (e.g., social and/or business networking websites, etc., business applications, games and other entertainment applications, etc.); and programming languages, etc., while ensuring compatibility with changing technologies, parameters, protocols, standards, etc.

Throughout this document, terms like “logic”, “component”, “module”, “framework”, “engine”, “mechanism”, and the like, may be referenced interchangeably and include, by way of example, software, hardware, and/or any combination of software and hardware, such as firmware. In one example, “logic” may refer to or include a software component that is capable of working with one or more of an operating system (e.g., operating system 606), a graphics driver (e.g., graphics driver 616), etc., of a computing device, such as computing device 600. In another example, “logic” may refer to or include a hardware component that is capable of being physically installed along with or as part of one or more system hardware elements, such as an application processor (e.g., CPU 612), a graphics processor (e.g., GPU 614), etc., of a computing device, such as computing device 600. In yet another embodiment, “logic” may refer to or include a firmware component that is capable of being part of system firmware, such as firmware of an application processor (e.g., CPU 612) or a graphics processor (e.g., GPU 614), etc., of a computing device, such as computing device 600.

Further, any use of a particular brand, word, term, phrase, name, and/or acronym, such as “GPU”, “GPU domain”, “GPGPU”, “CPU”, “CPU domain”, “graphics driver”, “workload”, “application”, “graphics pipeline”, “pipeline processes”, “slice”, “sub-slice”, “dual subs-slice”, “DSS”, “streaming multiprocessor”, “SM”, “SMM”, “load-balancing”, “migrating”, “copy engine”, “shared local memory”, “SLM”, “virtual machine”, “virtual function”, “API”, “3D API”, “OpenGL®”, “DirectX®”, “hardware”, “software”, “agent”, “graphics driver”, “kernel mode graphics driver”, “user-mode driver”, “user-mode driver framework”, “buffer”, “graphics buffer”, “task”, “process”, “operation”, “software application”, “game”, etc., should not be read to limit embodiments to software or devices that carry that label in products or in literature external to this document.

It is contemplated that any number and type of components may be added to and/or removed from de-centralized mechanism 610 to facilitate various embodiments including adding, removing, and/or enhancing certain features. For brevity, clarity, and ease of understanding of de-centralized mechanism 610, many of the standard and/or known components, such as those of a computing device, are not shown or discussed here. It is contemplated that embodiments, as described herein, are not limited to any particular technology, topology, system, architecture, and/or standard and are dynamic enough to adopt and adapt to any future changes.

FIG. 8 illustrates a conventional framework 800 employing a centralized scheduler 801. As previously discussed, conventional techniques are limited to hosting and using centralized scheduler 801 to perform load-balancing of tasks for slices 805A, 805B, graphics processor 803, etc. Given the centrality and remoteness of centralized scheduler 801, it remains unaware of local realities and thus, when load-balancing issues are encountered, centralized scheduler 801 is not equipped to address or handle them, resulting in loss of balance and inefficiencies.

FIG. 9A illustrates a novel architectural placement 900 employing a de-centralized load-balancing mechanism 610 according to one embodiment. For brevity, many of the details previously discussed with reference to FIGS. 1-8 may not be discussed or repeated hereafter. Any processes relating to architectural placement 900 may be performed by processing logic that may comprise hardware (e.g., circuitry, dedicated logic, programmable logic, etc.), software (such as instructions run on a processing device), or a combination thereof, as facilitated by de-centralized mechanism 610. The processes associated with architectural placement 900 may be illustrated or recited in linear sequences for brevity and clarity in presentation; however, it is contemplated that any number of them can be performed in parallel, asynchronously, or in different orders.

As previously discussed with reference to FIG. 7, in one embodiment, de-centralized mechanism 610 is placed at each of graphics processors 614, 914 and where de-centralized mechanism 610 serves as a local scheduler that works with other local components, such as copy engines 905A, 905B to perform local load-balancing that is accurate and efficient as opposed to using centralized scheduler 801 of FIG. 8. For brevity, many of the details discussed with reference to FIG. 7 are not repeated here.

As further discussed with reference to FIG. 7, in one embodiment, this novel technique of localized load-balancing may apply to slices 901A, 901B, 903A, 903B, representing SMs, SMMs, etc., or directly to graphics processors 614, 914 such that local load-balancing is performed using de-centralized mechanism 610 and any essential data, information, etc., is communicated and migrated between graphics processors 614 and 914 and/or slices 901A, 903A and 901B, 903B, respectively, using a local link, such as local bus 907.

FIG. 9B illustrates a novel architectural placement 950 employing a de-centralized load-balancing mechanism 610 according to one embodiment. For brevity, many of the details previously discussed with reference to FIGS. 1-9A may not be discussed or repeated hereafter. Any processes relating to architectural placement 950 may be performed by processing logic that may comprise hardware (e.g., circuitry, dedicated logic, programmable logic, etc.), software (such as instructions run on a processing device), or a combination thereof, as facilitated by de-centralized mechanism 610. The processes associated with architectural placement 950 may be illustrated or recited in linear sequences for brevity and clarity in presentation; however, it is contemplated that any number of them can be performed in parallel, asynchronously, or in different orders.

As illustrated, graphics processor 614 includes multiple SMs/SMMs 901A, 903A, where each of SMMs 901A, 903A includes a set of hardware threads, barriers, 953A, 953B, SLMs 951A, 951B, and/or local caches 955A, 955B. In one embodiment, as illustrated, de-centralized mechanism 610 may be common to all SMMs 901A, 903A and responsible for scheduling new threads or thread groups to the SMM hardware threads of SMMs 901A, 903A through and using hardware engine for thread state migration (“thread engine”) 957. In one embodiment, de-centralized mechanism 610 is further to monitor or keep track of all the resource allocations in SMMs 901A, 903A, including SLMs 951A, 951B, barriers 953A, 953B, etc.

For example, using detection/migration logic 701 of FIG. 7, de-centralized mechanism 610 periodically (or continuously or on-demand) monitor the hardware resource utilization, such as thread utilization per SMM 901A, 903A, SLM space allocated and free per SMM 901A, 903A and a number of free barriers 953A, 953B per SMM 951A, 951B.

In one embodiment, de-centralized mechanism 610 may perform the following: if detected by detection/migration logic 701 of FIG. 7 that the hardware thread utilization across SMMs 951A, 951B is significantly out-of-balance, it may trigger migration of one or more threads or thread groups from one SMM to another, such as from 901A to 903A, to re-balance the load. To achieve this, the local hardware may be used to support a checkpoint-and-migration mechanism as facilitated by detection/migration logic 701 of FIG. 7. Further, a new fixed-function unit may be added to save the thread's state (e.g., hardware registers, SLMs 951A, 951B, barriers 953A, 953B, etc.) of one thread or thread group running in SMM 901A and reload that state in a different SMM 903A.

Further, in one embodiment, as facilitated by detection/migration logic 701 of FIG. 7, de-centralized mechanism 610 detects that the SLM space in one or more SMMs 901A, 903A is significantly fragmented, which might prevent allocation of any new threads or thread groups, de-centralized mechanism 610 may perform a de-fragmentation action such that all threads in a thread group may be temporarily halted, while the SLM base address (such as stored in a hardware register) may be updated and then, the threads may be restarted.

FIG. 9C illustrates SLM de-fragmentation operation 970 according to one embodiment. For example, as illustrated, fragmented SLM 971 shows a space that is not continuous, such as TG0 973 of 16 KB, followed by free space of 8 KB, followed by TG1 975 of 16 KB, followed by another free space of 8 KB, followed by TG 2 977 of 16 KB, followed by another free space of 16 KB, and so forth. In one embodiment, using scheduling/loading logic 705 of de-centralized mechanism 610, a de-fragmentation operation is triggered such that in the illustrated embodiment, TG1 973 and TG2 975 of SLM 971 are moved up or migrated to create a continuous 32 KB space in SLM 981, where TG0 971, TG1 973, TG2 975 are continuous without any of the free space between them, leading to new free space 989 of 32 KB where a new TG of 32 KB may be scheduled by scheduling/loading logic 705. In other words, without this novel technique for facilitating de-fragmentation operation, this arrangement would not be feasible.

This novel technique has been described above with reference to specific embodiments. Persons skilled in the art, however, will understand that various modifications and changes may be made thereto without departing from the broader spirit and scope as set forth in the appended claims. The foregoing description and drawings are, accordingly, to be regarded in an illustrative rather than a restrictive sense.

System Overview II

FIG. 10 is a block diagram of a processing system 1000, according to an embodiment. In various embodiments, the system 1000 includes one or more processors 1002 and one or more graphics processors 1008, and may be a single processor desktop system, a multiprocessor workstation system, or a server system having a large number of processors 1002 or processor cores 1007. In on embodiment, the system 1000 is a processing platform incorporated within a system-on-a-chip (SoC) integrated circuit for use in mobile, handheld, or embedded devices.

An embodiment of system 1000 can include, or be incorporated within a server-based gaming platform, a game console, including a game and media console, a mobile gaming console, a handheld game console, or an online game console. In some embodiments system 1000 is a mobile phone, smart phone, tablet computing device or mobile Internet device. Data processing system 1000 can also include, couple with, or be integrated within a wearable device, such as a smart watch wearable device, smart eyewear device, augmented reality device, or virtual reality device. In some embodiments, data processing system 1000 is a television or set top box device having one or more processors 1002 and a graphical interface generated by one or more graphics processors 1008.

In some embodiments, the one or more processors 1002 each include one or more processor cores 1007 to process instructions which, when executed, perform operations for system and user software. In some embodiments, each of the one or more processor cores 1007 is configured to process a specific instruction set 1009. In some embodiments, instruction set 1009 may facilitate Complex Instruction Set Computing (CISC), Reduced Instruction Set Computing (RISC), or computing via a Very Long Instruction Word (VLIW). Multiple processor cores 1007 may each process a different instruction set 1009, which may include instructions to facilitate the emulation of other instruction sets. Processor core 1007 may also include other processing devices, such a Digital Signal Processor (DSP).

In some embodiments, the processor 1002 includes cache memory 1004. Depending on the architecture, the processor 1002 can have a single internal cache or multiple levels of internal cache. In some embodiments, the cache memory is shared among various components of the processor 1002. In some embodiments, the processor 1002 also uses an external cache (e.g., a Level-3 (L3) cache or Last Level Cache (LLC)) (not shown), which may be shared among processor cores 1007 using known cache coherency techniques. A register file 1006 is additionally included in processor 1002 which may include different types of registers for storing different types of data (e.g., integer registers, floating point registers, status registers, and an instruction pointer register). Some registers may be general-purpose registers, while other registers may be specific to the design of the processor 1002.

In some embodiments, processor 1002 is coupled to a processor bus 1010 to transmit communication signals such as address, data, or control signals between processor 1002 and other components in system 1000. In one embodiment, the system 1000 uses an exemplary ‘hub’ system architecture, including a memory controller hub 1016 and an Input Output (I/O) controller hub 1030. A memory controller hub 1016 facilitates communication between a memory device and other components of system 1000, while an I/O Controller Hub (ICH) 1030 provides connections to I/O devices via a local I/O bus. In one embodiment, the logic of the memory controller hub 1016 is integrated within the processor.

Memory device 1020 can be a dynamic random access memory (DRAM) device, a static random access memory (SRAM) device, flash memory device, phase-change memory device, or some other memory device having suitable performance to serve as process memory. In one embodiment, the memory device 1020 can operate as system memory for the system 1000, to store data 1022 and instructions 1021 for use when the one or more processors 1002 executes an application or process. Memory controller hub 1016 also couples with an optional external graphics processor 1012, which may communicate with the one or more graphics processors 1008 in processors 1002 to perform graphics and media operations.

In some embodiments, ICH 1030 enables peripherals to connect to memory device 1020 and processor 1002 via a high-speed I/O bus. The I/O peripherals include, but are not limited to, an audio controller 1046, a firmware interface 1028, a wireless transceiver 1026 (e.g., Wi-Fi, Bluetooth), a data storage device 1024 (e.g., hard disk drive, flash memory, etc.), and a legacy I/O controller 1040 for coupling legacy (e.g., Personal System 2 (PS/2)) devices to the system. One or more Universal Serial Bus (USB) controllers 1042 connect input devices, such as keyboard and mouse 1044 combinations. A network controller 1034 may also couple to ICH 1030. In some embodiments, a high-performance network controller (not shown) couples to processor bus 1010. It will be appreciated that the system 1000 shown is exemplary and not limiting, as other types of data processing systems that are differently configured may also be used. For example, the I/O controller hub 1030 may be integrated within the one or more processor 1002, or the memory controller hub 1016 and I/O controller hub 1030 may be integrated into a discreet external graphics processor, such as the external graphics processor 1012.

FIG. 11 is a block diagram of an embodiment of a processor 1100 having one or more processor cores 1102A-1102N, an integrated memory controller 1114, and an integrated graphics processor 1108. Those elements of FIG. 11 having the same reference numbers (or names) as the elements of any other figure herein can operate or function in any manner similar to that described elsewhere herein, but are not limited to such. Processor 1100 can include additional cores up to and including additional core 1102N represented by the dashed lined boxes. Each of processor cores 1102A-1102N includes one or more internal cache units 1104A-1104N. In some embodiments, each processor core also has access to one or more shared cached units 1106.

The internal cache units 1104A-1104N and shared cache units 1106 represent a cache memory hierarchy within the processor 1100. The cache memory hierarchy may include at least one level of instruction and data cache within each processor core and one or more levels of shared mid-level cache, such as a Level 2 (L2), Level 3 (L3), Level 4 (L4), or other levels of cache, where the highest level of cache before external memory is classified as the LLC. In some embodiments, cache coherency logic maintains coherency between the various cache units 1106 and 1104A-1104N.

In some embodiments, processor 1100 may also include a set of one or more bus controller units 1116 and a system agent core 1110. The one or more bus controller units 1116 manage a set of peripheral buses, such as one or more Peripheral Component Interconnect buses (e.g., PCI, PCI Express). System agent core 1110 provides management functionality for the various processor components. In some embodiments, system agent core 1110 includes one or more integrated memory controllers 1114 to manage access to various external memory devices (not shown).

In some embodiments, one or more of the processor cores 1102A-1102N include support for simultaneous multi-threading. In such embodiment, the system agent core 1110 includes components for coordinating and operating cores 1102A-1102N during multi-threaded processing. System agent core 1110 may additionally include a power control unit (PCU), which includes logic and components to regulate the power state of processor cores 1102A-1102N and graphics processor 1108.

In some embodiments, processor 1100 additionally includes graphics processor 1108 to execute graphics processing operations. In some embodiments, the graphics processor 1108 couples with the set of shared cache units 1106, and the system agent core 1110, including the one or more integrated memory controllers 1114. In some embodiments, a display controller 1111 is coupled with the graphics processor 1108 to drive graphics processor output to one or more coupled displays. In some embodiments, display controller 1111 may be a separate module coupled with the graphics processor via at least one interconnect, or may be integrated within the graphics processor 1108 or system agent core 1110.

In some embodiments, a ring based interconnect unit 1112 is used to couple the internal components of the processor 1100. However, an alternative interconnect unit may be used, such as a point-to-point interconnect, a switched interconnect, or other techniques, including techniques well known in the art. In some embodiments, graphics processor 1108 couples with the ring interconnect 1112 via an I/O link 1113.

The exemplary I/O link 1113 represents at least one of multiple varieties of I/O interconnects, including an on-package I/O interconnect which facilitates communication between various processor components and a high-performance embedded memory module 1118, such as an eDRAM module. In some embodiments, each of the processor cores 1102-1102N and graphics processor 1108 use embedded memory modules 1118 as a shared Last Level Cache.

In some embodiments, processor cores 1102A-1102N are homogenous cores executing the same instruction set architecture. In another embodiment, processor cores 1102A-1102N are heterogeneous in terms of instruction set architecture (ISA), where one or more of processor cores 1102A-N execute a first instruction set, while at least one of the other cores executes a subset of the first instruction set or a different instruction set. In one embodiment processor cores 1102A-1102N are heterogeneous in terms of microarchitecture, where one or more cores having a relatively higher power consumption couple with one or more power cores having a lower power consumption. Additionally, processor 1100 can be implemented on one or more chips or as an SoC integrated circuit having the illustrated components, in addition to other components.

FIG. 12 is a block diagram of a graphics processor 1200, which may be a discrete graphics processing unit, or may be a graphics processor integrated with a plurality of processing cores. In some embodiments, the graphics processor communicates via a memory mapped I/O interface to registers on the graphics processor and with commands placed into the processor memory. In some embodiments, graphics processor 1200 includes a memory interface 1214 to access memory. Memory interface 1214 can be an interface to local memory, one or more internal caches, one or more shared external caches, and/or to system memory.

In some embodiments, graphics processor 1200 also includes a display controller 1202 to drive display output data to a display device 1220. Display controller 1202 includes hardware for one or more overlay planes for the display and composition of multiple layers of video or user interface elements. In some embodiments, graphics processor 1200 includes a video codec engine 1206 to encode, decode, or transcode media to, from, or between one or more media encoding formats, including, but not limited to Moving Picture Experts Group (MPEG) formats such as MPEG-2, Advanced Video Coding (AVC) formats such as H.264/MPEG-4 AVC, as well as the Society of Motion Picture & Television Engineers (SMPTE) 421M/VC-1, and Joint Photographic Experts Group (JPEG) formats such as JPEG, and Motion JPEG (MJPEG) formats.

In some embodiments, graphics processor 1200 includes a block image transfer (BLIT) engine 1204 to perform two-dimensional (2D) rasterizer operations including, for example, bit-boundary block transfers. However, in one embodiment, 2D graphics operations are performed using one or more components of graphics processing engine (GPE) 1210. In some embodiments, graphics processing engine 1210 is a compute engine for performing graphics operations, including three-dimensional (3D) graphics operations and media operations.

In some embodiments, GPE 1210 includes a 3D pipeline 1212 for performing 3D operations, such as rendering three-dimensional images and scenes using processing functions that act upon 3D primitive shapes (e.g., rectangle, triangle, etc.). The 3D pipeline 1212 includes programmable and fixed function elements that perform various tasks within the element and/or spawn execution threads to a 3D/Media sub-system 1215. While 3D pipeline 1212 can be used to perform media operations, an embodiment of GPE 1210 also includes a media pipeline 1216 that is specifically used to perform media operations, such as video post-processing and image enhancement.

In some embodiments, media pipeline 1216 includes fixed function or programmable logic units to perform one or more specialized media operations, such as video decode acceleration, video de-interlacing, and video encode acceleration in place of, or on behalf of video codec engine 1206. In some embodiments, media pipeline 1216 additionally includes a thread spawning unit to spawn threads for execution on 3D/Media sub-system 1215. The spawned threads perform computations for the media operations on one or more graphics execution units included in 3D/Media sub-system 1215.

In some embodiments, 3D/Media subsystem 1215 includes logic for executing threads spawned by 3D pipeline 1212 and media pipeline 1216. In one embodiment, the pipelines send thread execution requests to 3D/Media subsystem 1215, which includes thread dispatch logic for arbitrating and dispatching the various requests to available thread execution resources. The execution resources include an array of graphics execution units to process the 3D and media threads. In some embodiments, 3D/Media subsystem 1215 includes one or more internal caches for thread instructions and data. In some embodiments, the subsystem also includes shared memory, including registers and addressable memory, to share data between threads and to store output data.

3D/Media Processing

FIG. 13 is a block diagram of a graphics processing engine 1310 of a graphics processor in accordance with some embodiments. In one embodiment, the graphics processing engine (GPE) 1310 is a version of the GPE 1210 shown in FIG. 12. Elements of FIG. 13 having the same reference numbers (or names) as the elements of any other figure herein can operate or function in any manner similar to that described elsewhere herein, but are not limited to such. For example, the 3D pipeline 1212 and media pipeline 1216 of FIG. 12 are illustrated. The media pipeline 1216 is optional in some embodiments of the GPE 1310 and may not be explicitly included within the GPE 1310. For example, and in at least one embodiment, a separate media and/or image processor is coupled to the GPE 1310.

In some embodiments, GPE 1310 couples with or includes a command streamer 1303, which provides a command stream to the 3D pipeline 1212 and/or media pipelines 1216. In some embodiments, command streamer 1303 is coupled with memory, which can be system memory, or one or more of internal cache memory and shared cache memory. In some embodiments, command streamer 1303 receives commands from the memory and sends the commands to 3D pipeline 1212 and/or media pipeline 1216. The commands are directives fetched from a ring buffer, which stores commands for the 3D pipeline 1212 and media pipeline 1216. In one embodiment, the ring buffer can additionally include batch command buffers storing batches of multiple commands. The commands for the 3D pipeline 1212 can also include references to data stored in memory, such as but not limited to vertex and geometry data for the 3D pipeline 1212 and/or image data and memory objects for the media pipeline 1216. The 3D pipeline 1212 and media pipeline 1216 process the commands and data by performing operations via logic within the respective pipelines or by dispatching one or more execution threads to a graphics core array 1314.

In various embodiments, the 3D pipeline 1212 can execute one or more shader programs, such as vertex shaders, geometry shaders, pixel shaders, fragment shaders, compute shaders, or other shader programs, by processing the instructions and dispatching execution threads to the graphics core array 1314. The graphics core array 1314 provides a unified block of execution resources. Multi-purpose execution logic (e.g., execution units) within the graphic core array 1314 includes support for various 3D API shader languages and can execute multiple simultaneous execution threads associated with multiple shaders.

In some embodiments, the graphics core array 1314 also includes execution logic to perform media functions, such as video and/or image processing. In one embodiment, the execution units additionally include general-purpose logic that is programmable to perform parallel general purpose computational operations, in addition to graphics processing operations. The general-purpose logic can perform processing operations in parallel or in conjunction with general purpose logic within the processor core(s) 1007 of FIG. 10 or core 1102A-1102N as in FIG. 11.

Output data generated by threads executing on the graphics core array 1314 can output data to memory in a unified return buffer (URB) 1318. The URB 1318 can store data for multiple threads. In some embodiments, the URB 1318 may be used to send data between different threads executing on the graphics core array 1314. In some embodiments, the URB 1318 may additionally be used for synchronization between threads on the graphics core array and fixed function logic within the shared function logic 1320.

In some embodiments, graphics core array 1314 is scalable, such that the array includes a variable number of graphics cores, each having a variable number of execution units based on the target power and performance level of GPE 1310. In one embodiment, the execution resources are dynamically scalable, such that execution resources may be enabled or disabled as needed.

The graphics core array 1314 couples with shared function logic 1320 that includes multiple resources that are shared between the graphics cores in the graphics core array. The shared functions within the shared function logic 1320 are hardware logic units that provide specialized supplemental functionality to the graphics core array 1314. In various embodiments, shared function logic 1320 includes but is not limited to sampler 1321, math 1322, and inter-thread communication (ITC) 1323 logic. Additionally, some embodiments implement one or more cache(s) 1325 within the shared function logic 1320. A shared function is implemented where the demand for a given specialized function is insufficient for inclusion within the graphics core array 1314. Instead a single instantiation of that specialized function is implemented as a stand-alone entity in the shared function logic 1320 and shared among the execution resources within the graphics core array 1314. The precise set of functions that are shared between the graphics core array 1314 and included within the graphics core array 1314 varies between embodiments.

FIG. 14 is a block diagram of another embodiment of a graphics processor 1400. Elements of FIG. 14 having the same reference numbers (or names) as the elements of any other figure herein can operate or function in any manner similar to that described elsewhere herein, but are not limited to such.

In some embodiments, graphics processor 1400 includes a ring interconnect 1402, a pipeline front-end 1404, a media engine 1437, and graphics cores 1480A-1480N. In some embodiments, ring interconnect 1402 couples the graphics processor to other processing units, including other graphics processors or one or more general-purpose processor cores. In some embodiments, the graphics processor is one of many processors integrated within a multi-core processing system.

In some embodiments, graphics processor 1400 receives batches of commands via ring interconnect 1402. The incoming commands are interpreted by a command streamer 1403 in the pipeline front-end 1404. In some embodiments, graphics processor 1400 includes scalable execution logic to perform 3D geometry processing and media processing via the graphics core(s) 1480A-1480N. For 3D geometry processing commands, command streamer 1403 supplies commands to geometry pipeline 1436. For at least some media processing commands, command streamer 1403 supplies the commands to a video front end 1434, which couples with a media engine 1437. In some embodiments, media engine 1437 includes a Video Quality Engine (VQE) 1430 for video and image post-processing and a multi-format encode/decode (MFX) 1433 engine to provide hardware-accelerated media data encode and decode. In some embodiments, geometry pipeline 1436 and media engine 1437 each generate execution threads for the thread execution resources provided by at least one graphics core 1480A.

In some embodiments, graphics processor 1400 includes scalable thread execution resources featuring modular cores 1480A-1480N (sometimes referred to as core slices), each having multiple sub-cores 1450A-1450N, 1460A-1460N (sometimes referred to as core sub-slices). In some embodiments, graphics processor 1400 can have any number of graphics cores 1480A through 1480N. In some embodiments, graphics processor 1400 includes a graphics core 1480A having at least a first sub-core 1450A and a second core sub-core 1460A. In other embodiments, the graphics processor is a low power processor with a single sub-core (e.g., 1450A). In some embodiments, graphics processor 1400 includes multiple graphics cores 1480A-1480N, each including a set of first sub-cores 1450A-1450N and a set of second sub-cores 1460A-1460N. Each sub-core in the set of first sub-cores 1450A-1450N includes at least a first set of execution units 1452A-1452N and media/texture samplers 1454A-1454N. Each sub-core in the set of second sub-cores 1460A-1460N includes at least a second set of execution units 1462A-1462N and samplers 1464A-1464N. In some embodiments, each sub-core 1450A-1450N, 1460A-1460N shares a set of shared resources 1470A-1470N. In some embodiments, the shared resources include shared cache memory and pixel operation logic. Other shared resources may also be included in the various embodiments of the graphics processor.

Execution Logic

FIG. 15 illustrates thread execution logic 1500 including an array of processing elements employed in some embodiments of a GPE. Elements of FIG. 15 having the same reference numbers (or names) as the elements of any other figure herein can operate or function in any manner similar to that described elsewhere herein, but are not limited to such.

In some embodiments, thread execution logic 1500 includes a pixel shader 1502, a thread dispatcher 1504, instruction cache 1506, a scalable execution unit array including a plurality of execution units 1508A-608N, a sampler 1510, a data cache 1512, and a data port 1514. In one embodiment, the included components are interconnected via an interconnect fabric that links to each of the components. In some embodiments, thread execution logic 1500 includes one or more connections to memory, such as system memory or cache memory, through one or more of instruction cache 1506, data port 1514, sampler 1510, and execution unit array 1508A-1508N. In some embodiments, each execution unit (e.g. 1508A) is an individual vector processor capable of executing multiple simultaneous threads and processing multiple data elements in parallel for each thread. In some embodiments, execution unit array 1508A-1508N includes any number individual execution units.

In some embodiments, execution unit array 1508A-1508N is primarily used to execute “shader” programs. In some embodiments, the execution units in array 1508A-1508N execute an instruction set that includes native support for many standard 3D graphics shader instructions, such that shader programs from graphics libraries (e.g., Direct 3D and OpenGL) are executed with a minimal translation. The execution units support vertex and geometry processing (e.g., vertex programs, geometry programs, vertex shaders), pixel processing (e.g., pixel shaders, fragment shaders) and general-purpose processing (e.g., compute and media shaders).

Each execution unit in execution unit array 1508A-1508N operates on arrays of data elements. The number of data elements is the “execution size,” or the number of channels for the instruction. An execution channel is a logical unit of execution for data element access, masking, and flow control within instructions. The number of channels may be independent of the number of physical Arithmetic Logic Units (ALUs) or Floating Point Units (FPUs) for a particular graphics processor. In some embodiments, execution units 1508A-1508N support integer and floating-point data types.

The execution unit instruction set includes single instruction multiple data (SIMD) or single instruction multiple thread (SIMT) instructions. The various data elements can be stored as a packed data type in a register and the execution unit will process the various elements based on the data size of the elements. For example, when operating on a 256-bit wide vector, the 256 bits of the vector are stored in a register and the execution unit operates on the vector as four separate 64-bit packed data elements (Quad-Word (QW) size data elements), eight separate 32-bit packed data elements (Double Word (DW) size data elements), sixteen separate 16-bit packed data elements (Word (W) size data elements), or thirty-two separate 8-bit data elements (byte (B) size data elements). However, different vector widths and register sizes are possible.

One or more internal instruction caches (e.g., 1506) are included in the thread execution logic 1500 to cache thread instructions for the execution units. In some embodiments, one or more data caches (e.g., 1512) are included to cache thread data during thread execution. In some embodiments, sampler 1510 is included to provide texture sampling for 3D operations and media sampling for media operations. In some embodiments, sampler 1510 includes specialized texture or media sampling functionality to process texture or media data during the sampling process before providing the sampled data to an execution unit.

During execution, the graphics and media pipelines send thread initiation requests to thread execution logic 1500 via thread spawning and dispatch logic. In some embodiments, thread execution logic 1500 includes a local thread dispatcher 1504 that arbitrates thread initiation requests from the graphics and media pipelines and instantiates the requested threads on one or more execution units 1508A-1508N. For example, the geometry pipeline (e.g., 1436 of FIG. 14) dispatches vertex processing, tessellation, or geometry processing threads to thread execution logic 1500 (FIG. 15). In some embodiments, thread dispatcher 1504 can also process runtime thread spawning requests from the executing shader programs.

Once a group of geometric objects has been processed and rasterized into pixel data, pixel shader 1502 is invoked to further compute output information and cause results to be written to output surfaces (e.g., color buffers, depth buffers, stencil buffers, etc.). In some embodiments, pixel shader 1502 calculates the values of the various vertex attributes that are to be interpolated across the rasterized object. In some embodiments, pixel shader 1502 then executes an application programming interface (API)-supplied pixel shader program. To execute the pixel shader program, pixel shader 1502 dispatches threads to an execution unit (e.g., 1508A) via thread dispatcher 1504. In some embodiments, pixel shader 1502 uses texture sampling logic in sampler 1510 to access texture data in texture maps stored in memory. Arithmetic operations on the texture data and the input geometry data compute pixel color data for each geometric fragment, or discards one or more pixels from further processing.

In some embodiments, the data port 1514 provides a memory access mechanism for the thread execution logic 1500 output processed data to memory for processing on a graphics processor output pipeline. In some embodiments, the data port 1514 includes or couples to one or more cache memories (e.g., data cache 1512) to cache data for memory access via the data port.

FIG. 16 is a block diagram illustrating a graphics processor instruction formats 1600 according to some embodiments. In one or more embodiment, the graphics processor execution units support an instruction set having instructions in multiple formats. The solid lined boxes illustrate the components that are generally included in an execution unit instruction, while the dashed lines include components that are optional or that are only included in a sub-set of the instructions. In some embodiments, instruction format 1600 described and illustrated are macro-instructions, in that they are instructions supplied to the execution unit, as opposed to micro-operations resulting from instruction decode once the instruction is processed.

In some embodiments, the graphics processor execution units natively support instructions in a 128-bit instruction format 1610. A 64-bit compacted instruction format 1630 is available for some instructions based on the selected instruction, instruction options, and number of operands. The native 128-bit instruction format 1610 provides access to all instruction options, while some options and operations are restricted in the 64-bit instruction format 1630. The native instructions available in the 64-bit instruction format 1630 vary by embodiment. In some embodiments, the instruction is compacted in part using a set of index values in an index field 1613. The execution unit hardware references a set of compaction tables based on the index values and uses the compaction table outputs to reconstruct a native instruction in the 128-bit instruction format 1610.

For each format, instruction opcode 1612 defines the operation that the execution unit is to perform. The execution units execute each instruction in parallel across the multiple data elements of each operand. For example, in response to an add instruction the execution unit performs a simultaneous add operation across each color channel representing a texture element or picture element. By default, the execution unit performs each instruction across all data channels of the operands. In some embodiments, instruction control field 1614 enables control over certain execution options, such as channels selection (e.g., predication) and data channel order (e.g., swizzle). For 128-bit instructions 1610 an exec-size field 1616 limits the number of data channels that will be executed in parallel. In some embodiments, exec-size field 1616 is not available for use in the 64-bit compact instruction format 1630.

Some execution unit instructions have up to three operands including two source operands, src0 1620, src1 1622, and one destination 1618. In some embodiments, the execution units support dual destination instructions, where one of the destinations is implied. Data manipulation instructions can have a third source operand (e.g., SRC2 1624), where the instruction opcode 1612 determines the number of source operands. An instruction's last source operand can be an immediate (e.g., hard-coded) value passed with the instruction.

In some embodiments, the 128-bit instruction format 1610 includes an access/address mode information 1626 specifying, for example, whether direct register addressing mode or indirect register addressing mode is used. When direct register addressing mode is used, the register address of one or more operands is directly provided by bits in the instruction 1610.

In some embodiments, the 128-bit instruction format 1610 includes an access/address mode field 1626, which specifies an address mode and/or an access mode for the instruction. In one embodiment, the access mode to define a data access alignment for the instruction. Some embodiments support access modes including a 16-byte aligned access mode and a 1-byte aligned access mode, where the byte alignment of the access mode determines the access alignment of the instruction operands. For example, when in a first mode, the instruction 1610 may use byte-aligned addressing for source and destination operands and when in a second mode, the instruction 1610 may use 16-byte-aligned addressing for all source and destination operands.

In one embodiment, the address mode portion of the access/address mode field 1626 determines whether the instruction is to use direct or indirect addressing. When direct register addressing mode is used bits in the instruction 1610 directly provide the register address of one or more operands. When indirect register addressing mode is used, the register address of one or more operands may be computed based on an address register value and an address immediate field in the instruction.

In some embodiments, instructions are grouped based on opcode 1612 bit-fields to simplify Opcode decode 1640. For an 8-bit opcode, bits 4, 5, and 6 allow the execution unit to determine the type of opcode. The precise opcode grouping shown is merely an example. In some embodiments, a move and logic opcode group 1642 includes data movement and logic instructions (e.g., move (mov), compare (cmp)). In some embodiments, move and logic group 1642 shares the five most significant bits (MSB), where move (mov) instructions are in the form of 0000xxxxb and logic instructions are in the form of 0001xxxxb. A flow control instruction group 1644 (e.g., call, jump (jmp)) includes instructions in the form of 0010xxxxb (e.g., 0x20). A miscellaneous instruction group 1646 includes a mix of instructions, including synchronization instructions (e.g., wait, send) in the form of 0011xxxxb (e.g., 0x30). A parallel math instruction group 1648 includes component-wise arithmetic instructions (e.g., add, multiply (mul)) in the form of 0100xxxxb (e.g., 0x40). The parallel math group 1648 performs the arithmetic operations in parallel across data channels. The vector math group 1650 includes arithmetic instructions (e.g., dp4) in the form of 0101xxxxb (e.g., 0x50). The vector math group performs arithmetic such as dot product calculations on vector operands.

Graphics Pipeline

FIG. 17 is a block diagram of another embodiment of a graphics processor 1700. Elements of FIG. 17 having the same reference numbers (or names) as the elements of any other figure herein can operate or function in any manner similar to that described elsewhere herein, but are not limited to such.

In some embodiments, graphics processor 1700 includes a graphics pipeline 1720, a media pipeline 1730, a display engine 1740, thread execution logic 1750, and a render output pipeline 1770. In some embodiments, graphics processor 1700 is a graphics processor within a multi-core processing system that includes one or more general-purpose processing cores. The graphics processor is controlled by register writes to one or more control registers (not shown) or via commands issued to graphics processor 1700 via a ring interconnect 1702. In some embodiments, ring interconnect 1702 couples graphics processor 1700 to other processing components, such as other graphics processors or general-purpose processors. Commands from ring interconnect 1702 are interpreted by a command streamer 1703, which supplies instructions to individual components of graphics pipeline 1720 or media pipeline 1730.

In some embodiments, command streamer 1703 directs the operation of a vertex fetcher 1705 that reads vertex data from memory and executes vertex-processing commands provided by command streamer 1703. In some embodiments, vertex fetcher 1705 provides vertex data to a vertex shader 1707, which performs coordinate space transformation and lighting operations to each vertex. In some embodiments, vertex fetcher 1705 and vertex shader 1707 execute vertex-processing instructions by dispatching execution threads to execution units 1752A, 1752B via a thread dispatcher 1731.

In some embodiments, execution units 1752A, 1752B are an array of vector processors having an instruction set for performing graphics and media operations. In some embodiments, execution units 1752A, 1752B have an attached L1 cache 1751 that is specific for each array or shared between the arrays. The cache can be configured as a data cache, an instruction cache, or a single cache that is partitioned to contain data and instructions in different partitions.

In some embodiments, graphics pipeline 1720 includes tessellation components to perform hardware-accelerated tessellation of 3D objects. In some embodiments, a programmable hull shader 1711 configures the tessellation operations. A programmable domain shader 1717 provides back-end evaluation of tessellation output. A tessellator 1713 operates at the direction of hull shader 1711 and contains special purpose logic to generate a set of detailed geometric objects based on a coarse geometric model that is provided as input to graphics pipeline 1720. In some embodiments, if tessellation is not used, tessellation components 1711, 1713, 1717 can be bypassed.

In some embodiments, complete geometric objects can be processed by a geometry shader 1719 via one or more threads dispatched to execution units 1752A, 1752B, or can proceed directly to the clipper 1729. In some embodiments, the geometry shader operates on entire geometric objects, rather than vertices or patches of vertices as in previous stages of the graphics pipeline. If the tessellation is disabled the geometry shader 1719 receives input from the vertex shader 1707. In some embodiments, geometry shader 1719 is programmable by a geometry shader program to perform geometry tessellation if the tessellation units are disabled.

Before rasterization, a clipper 1729 processes vertex data. The clipper 1729 may be a fixed function clipper or a programmable clipper having clipping and geometry shader functions. In some embodiments, a rasterizer and depth test component 1773 in the render output pipeline 1770 dispatches pixel shaders to convert the geometric objects into their per pixel representations. In some embodiments, pixel shader logic is included in thread execution logic 1750. In some embodiments, an application can bypass rasterization and access un-rasterized vertex data via a stream out unit 1723.

The graphics processor 1700 has an interconnect bus, interconnect fabric, or some other interconnect mechanism that allows data and message passing amongst the major components of the processor. In some embodiments, execution units 1752A, 1752B and associated cache(s) 1751, texture and media sampler 1754, and texture/sampler cache 1758 interconnect via a data port 1756 to perform memory access and communicate with render output pipeline components of the processor. In some embodiments, sampler 1754, caches 1751, 1758 and execution units 1752A, 1752B each have separate memory access paths.

In some embodiments, render output pipeline 1770 contains a rasterizer and depth test component 1773 that converts vertex-based objects into an associated pixel-based representation. In some embodiments, the render output pipeline 1770 includes a windower/masker unit to perform fixed function triangle and line rasterization. An associated render cache 1778 and depth cache 1779 are also available in some embodiments. A pixel operations component 1777 performs pixel-based operations on the data, though in some instances, pixel operations associated with 2D operations (e.g. bit block image transfers with blending) are performed by the 2D engine 1741, or substituted at display time by the display controller 1743 using overlay display planes. In some embodiments, a shared L3 cache 1775 is available to all graphics components, allowing the sharing of data without the use of main system memory.

In some embodiments, graphics processor media pipeline 1730 includes a media engine 1737 and a video front end 1734. In some embodiments, video front end 1734 receives pipeline commands from the command streamer 1703. In some embodiments, media pipeline 1730 includes a separate command streamer. In some embodiments, video front-end 1734 processes media commands before sending the command to the media engine 1737. In some embodiments, media engine 1737 includes thread spawning functionality to spawn threads for dispatch to thread execution logic 1750 via thread dispatcher 1731.

In some embodiments, graphics processor 1700 includes a display engine 1740. In some embodiments, display engine 1740 is external to processor 1700 and couples with the graphics processor via the ring interconnect 1702, or some other interconnect bus or fabric. In some embodiments, display engine 1740 includes a 2D engine 1741 and a display controller 1743. In some embodiments, display engine 1740 contains special purpose logic capable of operating independently of the 3D pipeline. In some embodiments, display controller 1743 couples with a display device (not shown), which may be a system integrated display device, as in a laptop computer, or an external display device attached via a display device connector.

In some embodiments, graphics pipeline 1720 and media pipeline 1730 are configurable to perform operations based on multiple graphics and media programming interfaces and are not specific to any one application programming interface (API). In some embodiments, driver software for the graphics processor translates API calls that are specific to a particular graphics or media library into commands that can be processed by the graphics processor. In some embodiments, support is provided for the Open Graphics Library (OpenGL) and Open Computing Language (OpenCL) from the Khronos Group, the Direct3D library from the Microsoft Corporation, or support may be provided to both OpenGL and D3D. Support may also be provided for the Open Source Computer Vision Library (OpenCV). A future API with a compatible 3D pipeline would also be supported if a mapping can be made from the pipeline of the future API to the pipeline of the graphics processor.

Graphics Pipeline Programming

FIG. 18A is a block diagram illustrating a graphics processor command format 1800 according to some embodiments. FIG. 18B is a block diagram illustrating a graphics processor command sequence 1810 according to an embodiment. The solid lined boxes in FIG. 18A illustrate the components that are generally included in a graphics command while the dashed lines include components that are optional or that are only included in a sub-set of the graphics commands. The exemplary graphics processor command format 1800 of FIG. 18A includes data fields to identify a target client 1802 of the command, a command operation code (opcode) 1804, and the relevant data 1806 for the command. A sub-opcode 1805 and a command size 1808 are also included in some commands.

In some embodiments, client 1802 specifies the client unit of the graphics device that processes the command data. In some embodiments, a graphics processor command parser examines the client field of each command to condition the further processing of the command and route the command data to the appropriate client unit. In some embodiments, the graphics processor client units include a memory interface unit, a render unit, a 2D unit, a 3D unit, and a media unit. Each client unit has a corresponding processing pipeline that processes the commands. Once the command is received by the client unit, the client unit reads the opcode 1804 and, if present, sub-opcode 1805 to determine the operation to perform. The client unit performs the command using information in data field 1806. For some commands an explicit command size 1808 is expected to specify the size of the command. In some embodiments, the command parser automatically determines the size of at least some of the commands based on the command opcode. In some embodiments, commands are aligned via multiples of a double word.

The flow diagram in FIG. 18B shows an exemplary graphics processor command sequence 1810. In some embodiments, software or firmware of a data processing system that features an embodiment of a graphics processor uses a version of the command sequence shown to set up, execute, and terminate a set of graphics operations. A sample command sequence is shown and described for purposes of example only as embodiments are not limited to these specific commands or to this command sequence. Moreover, the commands may be issued as batch of commands in a command sequence, such that the graphics processor will process the sequence of commands in at least partially concurrence.

In some embodiments, the graphics processor command sequence 1810 may begin with a pipeline flush command 1812 to cause any active graphics pipeline to complete the currently pending commands for the pipeline. In some embodiments, the 3D pipeline 1822 and the media pipeline 1824 do not operate concurrently. The pipeline flush is performed to cause the active graphics pipeline to complete any pending commands. In response to a pipeline flush, the command parser for the graphics processor will pause command processing until the active drawing engines complete pending operations and the relevant read caches are invalidated. Optionally, any data in the render cache that is marked ‘dirty’ can be flushed to memory. In some embodiments, pipeline flush command 1812 can be used for pipeline synchronization or before placing the graphics processor into a low power state.

In some embodiments, a pipeline select command 1813 is used when a command sequence requires the graphics processor to explicitly switch between pipelines. In some embodiments, a pipeline select command 1813 is required only once within an execution context before issuing pipeline commands unless the context is to issue commands for both pipelines. In some embodiments, a pipeline flush command is 1812 is required immediately before a pipeline switch via the pipeline select command 1813.

In some embodiments, a pipeline control command 1814 configures a graphics pipeline for operation and is used to program the 3D pipeline 1822 and the media pipeline 1824. In some embodiments, pipeline control command 1814 configures the pipeline state for the active pipeline. In one embodiment, the pipeline control command 1814 is used for pipeline synchronization and to clear data from one or more cache memories within the active pipeline before processing a batch of commands.

In some embodiments, commands for the return buffer state 1816 are used to configure a set of return buffers for the respective pipelines to write data. Some pipeline operations require the allocation, selection, or configuration of one or more return buffers into which the operations write intermediate data during processing. In some embodiments, the graphics processor also uses one or more return buffers to store output data and to perform cross thread communication. In some embodiments, configuring the return buffer state 1816 includes selecting the size and number of return buffers to use for a set of pipeline operations.

The remaining commands in the command sequence differ based on the active pipeline for operations. Based on a pipeline determination 1820, the command sequence is tailored to the 3D pipeline 1822 beginning with the 3D pipeline state 1830, or the media pipeline 1824 beginning at the media pipeline state 1840.

The commands for the 3D pipeline state 1830 include 3D state setting commands for vertex buffer state, vertex element state, constant color state, depth buffer state, and other state variables that are to be configured before 3D primitive commands are processed. The values of these commands are determined at least in part based the particular 3D API in use. In some embodiments, 3D pipeline state 1830 commands are also able to selectively disable or bypass certain pipeline elements if those elements will not be used.

In some embodiments, 3D primitive 1832 command is used to submit 3D primitives to be processed by the 3D pipeline. Commands and associated parameters that are passed to the graphics processor via the 3D primitive 1832 command are forwarded to the vertex fetch function in the graphics pipeline. The vertex fetch function uses the 3D primitive 1832 command data to generate vertex data structures. The vertex data structures are stored in one or more return buffers. In some embodiments, 3D primitive 1832 command is used to perform vertex operations on 3D primitives via vertex shaders. To process vertex shaders, 3D pipeline 1822 dispatches shader execution threads to graphics processor execution units.

In some embodiments, 3D pipeline 1822 is triggered via an execute 1834 command or event. In some embodiments, a register write triggers command execution. In some embodiments execution is triggered via a ‘go’ or ‘kick’ command in the command sequence. In one embodiment command execution is triggered using a pipeline synchronization command to flush the command sequence through the graphics pipeline. The 3D pipeline will perform geometry processing for the 3D primitives. Once operations are complete, the resulting geometric objects are rasterized and the pixel engine colors the resulting pixels. Additional commands to control pixel shading and pixel back end operations may also be included for those operations.

In some embodiments, the graphics processor command sequence 1810 follows the media pipeline 1824 path when performing media operations. In general, the specific use and manner of programming for the media pipeline 1824 depends on the media or compute operations to be performed. Specific media decode operations may be offloaded to the media pipeline during media decode. In some embodiments, the media pipeline can also be bypassed and media decode can be performed in whole or in part using resources provided by one or more general-purpose processing cores. In one embodiment, the media pipeline also includes elements for general-purpose graphics processor unit (GPGPU) operations, where the graphics processor is used to perform SIMD vector operations using computational shader programs that are not explicitly related to the rendering of graphics primitives.

In some embodiments, media pipeline 1824 is configured in a similar manner as the 3D pipeline 1822. A set of commands to configure the media pipeline state 1840 are dispatched or placed into a command queue before the media object commands 1842. In some embodiments, commands for the media pipeline state 1840 include data to configure the media pipeline elements that will be used to process the media objects. This includes data to configure the video decode and video encode logic within the media pipeline, such as encode or decode format. In some embodiments, commands for the media pipeline state 1840 also support the use of one or more pointers to “indirect” state elements that contain a batch of state settings.

In some embodiments, media object commands 1842 supply pointers to media objects for processing by the media pipeline. The media objects include memory buffers containing video data to be processed. In some embodiments, all media pipeline states must be valid before issuing a media object command 1842. Once the pipeline state is configured and media object commands 1842 are queued, the media pipeline 1824 is triggered via an execute command 1844 or an equivalent execute event (e.g., register write). Output from media pipeline 1824 may then be post processed by operations provided by the 3D pipeline 1822 or the media pipeline 1824. In some embodiments, GPGPU operations are configured and executed in a similar manner as media operations.

Graphics Software Architecture

FIG. 19 illustrates exemplary graphics software architecture for a data processing system 1900 according to some embodiments. In some embodiments, software architecture includes a 3D graphics application 1910, an operating system 1920, and at least one processor 1930. In some embodiments, processor 1930 includes a graphics processor 1932 and one or more general-purpose processor core(s) 1934. The graphics application 1910 and operating system 1920 each execute in the system memory 1950 of the data processing system.

In some embodiments, 3D graphics application 1910 contains one or more shader programs including shader instructions 1912. The shader language instructions may be in a high-level shader language, such as the High Level Shader Language (HLSL) or the OpenGL Shader Language (GLSL). The application also includes executable instructions 1914 in a machine language suitable for execution by the general-purpose processor core(s) 1934. The application also includes graphics objects 1916 defined by vertex data.

In some embodiments, operating system 1920 is a Microsoft® Windows® operating system from the Microsoft Corporation, a proprietary UNIX-like operating system, or an open source UNIX-like operating system using a variant of the Linux kernel. The operating system 1920 can support a graphics API 1922 such as the Direct3D API or the OpenGL API. When the Direct3D API is in use, the operating system 1920 uses a front-end shader compiler 1924 to compile any shader instructions 1912 in HLSL into a lower-level shader language. The compilation may be a just-in-time (JIT) compilation or the application can perform shader pre-compilation. In some embodiments, high-level shaders are compiled into low-level shaders during the compilation of the 3D graphics application 1910.

In some embodiments, user mode graphics driver 1926 contains a back-end shader compiler 1927 to convert the shader instructions 1912 into a hardware specific representation. When the OpenGL API is in use, shader instructions 1912 in the GLSL high-level language are passed to a user mode graphics driver 1926 for compilation. In some embodiments, user mode graphics driver 1926 uses operating system kernel mode functions 1928 to communicate with a kernel mode graphics driver 1929. In some embodiments, kernel mode graphics driver 1929 communicates with graphics processor 1932 to dispatch commands and instructions.

IP Core Implementations

One or more aspects of at least one embodiment may be implemented by representative code stored on a machine-readable medium which represents and/or defines logic within an integrated circuit such as a processor. For example, the machine-readable medium may include instructions which represent various logic within the processor. When read by a machine, the instructions may cause the machine to fabricate the logic to perform the techniques described herein. Such representations, known as “IP cores,” are reusable units of logic for an integrated circuit that may be stored on a tangible, machine-readable medium as a hardware model that describes the structure of the integrated circuit. The hardware model may be supplied to various customers or manufacturing facilities, which load the hardware model on fabrication machines that manufacture the integrated circuit. The integrated circuit may be fabricated such that the circuit performs operations described in association with any of the embodiments described herein.

FIG. 20 is a block diagram illustrating an IP core development system 2000 that may be used to manufacture an integrated circuit to perform operations according to an embodiment. The IP core development system 2000 may be used to generate modular, re-usable designs that can be incorporated into a larger design or used to construct an entire integrated circuit (e.g., an SOC integrated circuit). A design facility 2030 can generate a software simulation 2010 of an IP core design in a high-level programming language (e.g., C/C++). The software simulation 2010 can be used to design, test, and verify the behavior of the IP core using a simulation model 2012. The simulation model 2012 may include functional, behavioral, and/or timing simulations. A register transfer level (RTL) design 2015 can then be created or synthesized from the simulation model 2012. The RTL design 2015 is an abstraction of the behavior of the integrated circuit that models the flow of digital signals between hardware registers, including the associated logic performed using the modeled digital signals. In addition to an RTL design 2015, lower-level designs at the logic level or transistor level may also be created, designed, or synthesized. Thus, the particular details of the initial design and simulation may vary.

The RTL design 2015 or equivalent may be further synthesized by the design facility into a hardware model 2020, which may be in a hardware description language (HDL), or some other representation of physical design data. The HDL may be further simulated or tested to verify the IP core design. The IP core design can be stored for delivery to a 3^(rd) party fabrication facility 2065 using non-volatile memory 2040 (e.g., hard disk, flash memory, or any non-volatile storage medium). Alternatively, the IP core design may be transmitted (e.g., via the Internet) over a wired connection 2050 or wireless connection 2060. The fabrication facility 2065 may then fabricate an integrated circuit that is based at least in part on the IP core design. The fabricated integrated circuit can be configured to perform operations in accordance with at least one embodiment described herein.

Exemplary System on a Chip Integrated Circuit

FIGS. 21-23 illustrate exemplary integrated circuits and associated graphics processors that may be fabricated using one or more IP cores, according to various embodiments described herein. In addition to what is illustrated, other logic and circuits may be included, including additional graphics processors/cores, peripheral interface controllers, or general purpose processor cores.

FIG. 21 is a block diagram illustrating an exemplary system on a chip integrated circuit 2100 that may be fabricated using one or more IP cores, according to an embodiment. Exemplary integrated circuit 2100 includes one or more application processor(s) 2105 (e.g., CPUs), at least one graphics processor 2110, and may additionally include an image processor 2115 and/or a video processor 2120, any of which may be a modular IP core from the same or multiple different design facilities. Integrated circuit 2100 includes peripheral or bus logic including a USB controller 2125, UART controller 2130, an SPI/SDIO controller 2135, and an I²S/I²C controller 2140. Additionally, the integrated circuit can include a display device 2145 coupled to one or more of a high-definition multimedia interface (HDMI) controller 2150 and a mobile industry processor interface (MIPI) display interface 2155. Storage may be provided by a flash memory subsystem 2160 including flash memory and a flash memory controller. Memory interface may be provided via a memory controller 2165 for access to SDRAM or SRAM memory devices. Some integrated circuits additionally include an embedded security engine 2170.

FIG. 22 is a block diagram illustrating an exemplary graphics processor 2210 of a system on a chip integrated circuit that may be fabricated using one or more IP cores, according to an embodiment. Graphics processor 2210 can be a variant of the graphics processor 2110 of FIG. 21. Graphics processor 2210 includes a vertex processor 2205 and one or more fragment processor(s) 2215A-2215N (e.g., 2215A, 2215B, 2215C, 2215D, through 2215N−1, and 2215N). Graphics processor 2210 can execute different shader programs via separate logic, such that the vertex processor 2205 is optimized to execute operations for vertex shader programs, while the one or more fragment processor(s) 2215A-2215N execute fragment (e.g., pixel) shading operations for fragment or pixel shader programs. The vertex processor 2205 performs the vertex processing stage of the 3D graphics pipeline and generates primitives and vertex data. The fragment processor(s) 2215A-2215N use the primitive and vertex data generated by the vertex processor 2205 to produce a framebuffer that is displayed on a display device. In one embodiment, the fragment processor(s) 2215A-2215N are optimized to execute fragment shader programs as provided for in the OpenGL API, which may be used to perform similar operations as a pixel shader program as provided for in the Direct 3D API.

Graphics processor 2210 additionally includes one or more memory management units (MMUs) 2220A-2220B, cache(s) 2225A-2225B, and circuit interconnect(s) 2230A-2230B. The one or more MMU(s) 2220A-2220B provide for virtual to physical address mapping for graphics processor 2210, including for the vertex processor 2205 and/or fragment processor(s) 2215A-2215N, which may reference vertex or image/texture data stored in memory, in addition to vertex or image/texture data stored in the one or more cache(s) 2225A-2225B. In one embodiment, the one or more MMU(s) 2220A-2220B may be synchronized with other MMUs within the system, including one or more MMUs associated with the one or more application processor(s) 2105, image processor 2115, and/or video processor 2120 of FIG. 21, such that each processor 2105-2120 can participate in a shared or unified virtual memory system. The one or more circuit interconnect(s) 2230A-2230B enable graphics processor 2210 to interface with other IP cores within the SoC, either via an internal bus of the SoC or via a direct connection, according to embodiments.

FIG. 23 is a block diagram illustrating an additional exemplary graphics processor 2310 of a system on a chip integrated circuit that may be fabricated using one or more IP cores, according to an embodiment. Graphics processor 2310 can be a variant of the graphics processor 2110 of FIG. 21. Graphics processor 2310 includes the one or more MMU(s) 2220A-2220B, cache(s) 2225A-2225B, and circuit interconnect(s) 2230A-2230B of the integrated circuit 2200 of FIG. 22.

Graphics processor 2310 includes one or more shader core(s) 2315A-2315N (e.g., 2315A, 2315B, 2315C, 2315D, 2315E, 2315F, through 2215N−1, and 2215N), which provides for a unified shader core architecture in which a single core or type or core can execute all types of programmable shader code, including shader program code to implement vertex shaders, fragment shaders, and/or compute shaders. The exact number of shader cores present can vary among embodiments and implementations. Additionally, graphics processor 2310 includes an inter-core task manager 2305, which acts as a thread dispatcher to dispatch execution threads to one or more shader core(s) 2315A-2315N. Graphics processor 2310 additionally includes a tiling unit 2318 to accelerate tiling operations for tile-based rendering, in which rendering operations for a scene are subdivided in image space. Tile-based rendering can be used to exploit local spatial coherence within a scene or to optimize use of internal caches.

References to “one embodiment”, “an embodiment”, “example embodiment”, “various embodiments”, etc., indicate that the embodiment(s) so described may include particular features, structures, or characteristics, but not every embodiment necessarily includes the particular features, structures, or characteristics. Further, some embodiments may have some, all, or none of the features described for other embodiments.

In the foregoing specification, embodiments have been described with reference to specific exemplary embodiments thereof. It will, however, be evident that various modifications and changes may be made thereto without departing from the broader spirit and scope of embodiments as set forth in the appended claims. The Specification and drawings are, accordingly, to be regarded in an illustrative rather than a restrictive sense.

In the following description and claims, the term “coupled” along with its derivatives, may be used. “Coupled” is used to indicate that two or more elements co-operate or interact with each other, but they may or may not have intervening physical or electrical components between them.

As used in the claims, unless otherwise specified the use of the ordinal adjectives “first”, “second”, “third”, etc., to describe a common element, merely indicate that different instances of like elements are being referred to, and are not intended to imply that the elements so described must be in a given sequence, either temporally, spatially, in ranking, or in any other manner.

The following clauses and/or examples pertain to further embodiments or examples. Specifics in the examples may be used anywhere in one or more embodiments. The various features of the different embodiments or examples may be variously combined with some features included and others excluded to suit a variety of different applications. Examples may include subject matter such as a method, means for performing acts of the method, at least one machine-readable medium including instructions that, when performed by a machine cause the machine to performs acts of the method, or of an apparatus or system for facilitating hybrid communication according to embodiments and examples described herein.

Some embodiments pertain to Example 1 that includes an apparatus to localized load-balancing for processors in computing devices, the apparatus comprising: a processor to host a local load-balancing mechanism; detection/migration logic of the local load-balancing mechanism to monitor balancing of loads at the processor; and scheduling/loading logic to serve as a local scheduler to maintain de-centralized load-balancing at the processor and between the processor and other one or more processors.

Example 2 includes the subject matter of Example 1, wherein the detection/migration logic is further to detect lack of balance in distribution of the loads at the processor, wherein the lack of balance is due to one or more of a slice, a streaming multiprocessor (SM), and the processor.

Example 3 includes the subject matter of Examples 1-2, wherein the detection/migration logic is further to migrate one or more threads associated with the one or more of the slice, the SM, and the processor from a first location to a second location, if the lack of balance is detected.

Example 4 includes the subject matter of Examples 1-3, further comprising copy engine reference logic to copy thread context associated with one or more of the threads, wherein the copy reference logic is further to stream the thread context to the second location to prepare for migration of the one or more threads.

Example 5 includes the subject matter of Examples 1-4, further comprising communication/compatibility logic to synchronize the thread context copied at the second location with the one or more of the slice, the SM, and the processor to restart processing of the loads, wherein the scheduling/loading logic to initiate load-balancing based on the second location.

Example 6 includes the subject matter of Examples 1-5, wherein the detection/migration logic is further to facilitate migration of one or more of the slice and the SM within the processor having multiple slices or SMs in communication with one or more of hardware engine for thread state migration, one or more local caches, one or more barriers, and a shared local memory (SLM), wherein the detection/migration logic is further to detect fragmentation in the SLM, wherein the fragmentation refers to non-contiguous spaces within the SLM, and wherein the scheduling/loading logic is further to facilitate de-fragmentation of the SLM to create a contiguous space within the SLM.

Example 7 includes the subject matter of Examples 1-6, wherein the processor comprises a graphics processor that is co-located with an application processor on a common semiconductor package.

Some embodiments pertain to Example 8 that includes a method for facilitating localized load-balancing for processors in computing devices, the method comprising: hosting, at a processor, a local load-balancing mechanism; and serving as a local scheduler to maintain de-centralized load-balancing at the processor and between the processor and other one or more processors.

Example 9 includes the subject matter of Example 8, further comprising detecting lack of balance in distribution of the loads at the processor, wherein the lack of balance is due to one or more of a slice, a streaming multiprocessor (SM), and the processor.

Example 10 includes the subject matter of Examples 8-9, further comprising migrating one or more threads associated with the one or more of the slice, the SM, and the processor from a first location to a second location, if the lack of balance is detected.

Example 11 includes the subject matter of Examples 8-10, further comprising: copying thread context associated with one or more of the threads; and streaming the thread context to the second location to prepare for migration of the one or more threads.

Example 12 includes the subject matter of Examples 8-11, further comprising: synchronizing the thread context copied at the second location with the one or more of the slice, the SM, and the processor to restart processing of the loads; and initiating load-balancing based on the second location.

Example 13 includes the subject matter of Examples 8-12, further comprising: facilitating migration of one or more of the slice and the SM within the processor having multiple slices or SMs in communication with one or more of hardware engine for thread state migration, one or more local caches, one or more barriers, and a shared local memory (SLM); detecting fragmentation in the SLM, wherein the fragmentation refers to non-contiguous spaces within the SLM; and facilitating de-fragmentation of the SLM to create a contiguous space within the SLM.

Example 14 includes the subject matter of Examples 8-13, wherein the processor comprises a graphics processor that is co-located with an application processor on a common semiconductor package.

Some embodiments pertain to Example 15 that includes a graphics processing system comprising a computing device having memory coupled to a processor, the processor to host a local load-balancing mechanism; and serve as a local scheduler to maintain de-centralized load-balancing at the processor and between the processor and other one or more processors.

Example 16 includes the subject matter of Example 15, wherein the processor is further to detect lack of balance in distribution of the loads at the processor, wherein the lack of balance is due to one or more of a slice, a streaming multiprocessor (SM), and the processor.

Example 17 includes the subject matter of Example 15-16, wherein the processor is further to migrate one or more threads associated with the one or more of the slice, the SM, and the processor from a first location to a second location, if the lack of balance is detected.

Example 18 includes the subject matter of Examples 15-17, wherein the processor is further to: copy thread context associated with one or more of the threads; and stream the thread context to the second location to prepare for migration of the one or more threads.

Example 19 includes the subject matter of Example 15-18, wherein the processor is further to: synchronize the thread context copied at the second location with the one or more of the slice, the SM, and the processor to restart processing of the loads; and initiate load-balancing based on the second location.

Example 20 includes the subject matter of Example 15-19, wherein the processor is further to: facilitate migration of one or more of the slice and the SM within the processor having multiple slices or SMs in communication with one or more of hardware engine for thread state migration, one or more local caches, one or more barriers, and a shared local memory (SLM); detect fragmentation in the SLM, wherein the fragmentation refers to non-contiguous spaces within the SLM; and facilitate de-fragmentation of the SLM to create a contiguous space within the SLM.

Example 21 includes the subject matter of Example 15-20, wherein the processor comprises a graphics processor that is co-located with an application processor on a common semiconductor package.

Example 29 includes at least one non-transitory or tangible machine-readable medium comprising a plurality of instructions, when executed on a computing device, to implement or perform a method as claimed in any of claims or examples 8-14.

Example 30 includes at least one machine-readable medium comprising a plurality of instructions, when executed on a computing device, to implement or perform a method as claimed in any of claims or examples 8-14.

Example 31 includes a system comprising a mechanism to implement or perform a method as claimed in any of claims or examples 8-14.

Example 32 includes an apparatus comprising means for performing a method as claimed in any of claims or examples 8-14.

Example 33 includes a computing device arranged to implement or perform a method as claimed in any of claims or examples 8-14.

Example 34 includes a communications device arranged to implement or perform a method as claimed in any of claims or examples 8-14.

Example 35 includes at least one machine-readable medium comprising a plurality of instructions, when executed on a computing device, to implement or perform a method or realize an apparatus as claimed in any preceding claims.

Example 36 includes at least one non-transitory or tangible machine-readable medium comprising a plurality of instructions, when executed on a computing device, to implement or perform a method or realize an apparatus as claimed in any preceding claims.

Example 37 includes a system comprising a mechanism to implement or perform a method or realize an apparatus as claimed in any preceding claims.

Example 38 includes an apparatus comprising means to perform a method as claimed in any preceding claims.

Example 39 includes a computing device arranged to implement or perform a method or realize an apparatus as claimed in any preceding claims.

Example 40 includes a communications device arranged to implement or perform a method or realize an apparatus as claimed in any preceding claims.

The drawings and the forgoing description give examples of embodiments. Those skilled in the art will appreciate that one or more of the described elements may well be combined into a single functional element. Alternatively, certain elements may be split into multiple functional elements. Elements from one embodiment may be added to another embodiment. For example, orders of processes described herein may be changed and are not limited to the manner described herein. Moreover, the actions of any flow diagram need not be implemented in the order shown; nor do all of the acts necessarily need to be performed. Also, those acts that are not dependent on other acts may be performed in parallel with the other acts. The scope of embodiments is by no means limited by these specific examples. Numerous variations, whether explicitly given in the specification or not, such as differences in structure, dimension, and use of material, are possible. The scope of embodiments is at least as broad as given by the following claims. 

1-20. (canceled)
 21. An apparatus comprising: one or more processors including at least a first processor; and a shared local memory (SLM); wherein the first processor is to: schedule threads of a plurality of thread groups for processing, monitor SLM space, including monitoring for fragmentation of the plurality of thread groups in the SLM space, detect whether there is excessive fragmentation of the SLM space, and upon detection of excessive fragmentation of the SLM space, perform a de-fragmentation of the SLM space.
 22. The apparatus of claim 21, wherein detecting excessive fragmentation of the SLM space includes determining that the fragmentation of the SLM space will prevent scheduling of an additional thread or thread group for processing.
 23. The apparatus of claim 22, wherein the first processor is further to: schedule additional threads utilizing a portion of the SLM space that is reclaimed by the performance of the de fragmentation of the SLM space.
 24. The apparatus of claim 21, wherein the fragmentation of the SLM space comprises one or more non-contiguous portions of the SLM space between the plurality of thread groups.
 25. The apparatus of claim 21, wherein performing the de-fragmentation of the SLM space includes: halting threads of at least a first thread group o the plurality of thread groups; migrating the threads of the first thread groups to remove a free space between the first thread group and a second thread group in the SLM and create a contiguous space in the SLM space for the first thread group and the second thread group; and restarting the halted threads.
 26. The apparatus of claim 25, wherein performing the de-fragmentation of the SLM space includes migrating threads of two or more thread groups to create the contiguous space in the SLM space.
 27. The apparatus of claim 25, wherein performing the de-fragmentation of the SLM space includes updating an SLM base address in a hardware register.
 28. The apparatus of claim 21, wherein the first processor comprises a graphics processor that is co-located with an application processor on a common semiconductor package.
 29. A method comprising: scheduling threads of a plurality of thread groups for processing by a processor; monitoring space of a shared local memory (SLM), including monitoring for fragmentation of the plurality of thread groups in the SLM space; detecting whether there is excessive fragmentation of the SLM space; and upon detection of excessive fragmentation of the SLM space, performing a de-fragmentation of the SLM space.
 30. The method of claim 29, wherein detecting excessive fragmentation of the SLM space includes determining that the fragmentation of the SLM space will prevent scheduling of an additional thread or thread group for processing.
 31. The method of claim 30, further comprising: scheduling additional threads utilizing a portion of the SLM space that is reclaimed by the performance of the de fragmentation of the SLM space.
 32. The method of claim 29, wherein the fragmentation of the SLM space comprises one or more non-contiguous portions of the SLM space between the plurality of thread groups.
 33. The method of claim 29, wherein performing the de-fragmentation of the SLM space includes: halting threads of at least a first thread group o the plurality of thread groups; migrating the threads of the first thread groups to remove a free space between the first thread group and a second thread group in the SLM and create a contiguous space in the SLM space for the first thread group and the second thread group; and restarting the halted threads.
 34. The method of claim 33, wherein performing the de-fragmentation of the SLM space includes migrating threads of two or more thread groups to create the contiguous space in the SLM space.
 35. At least one non-transitory machine-readable medium comprising instructions that when executed by a computing device, cause the computing device to perform operations comprising: scheduling threads of a plurality of thread groups for processing by a processor; monitoring space of a shared local memory (SLM), including monitoring for fragmentation of the plurality of thread groups in the SLM space; detecting whether there is excessive fragmentation of the SLM space; and upon detection of excessive fragmentation of the SLM space, performing a de-fragmentation of the SLM space.
 36. The non-transitory machine-readable medium of claim 35, wherein detecting excessive fragmentation of the SLM space includes determining that the fragmentation of the SLM space will prevent scheduling of an additional thread or thread group for processing.
 37. The non-transitory machine-readable medium of claim 36, wherein the operations further comprise: scheduling additional threads utilizing a portion of the SLM space that is reclaimed by the performance of the de fragmentation of the SLM space.
 38. The non-transitory machine-readable medium of claim 35, wherein the fragmentation of the SLM space comprises one or more non-contiguous portions of the SLM space between the plurality of thread groups.
 39. The non-transitory machine-readable medium of claim 35, wherein performing the de-fragmentation of the SLM space includes: halting threads of at least a first thread group o the plurality of thread groups; migrating the threads of the first thread groups to remove a free space between the first thread group and a second thread group in the SLM and create a contiguous space in the SLM space for the first thread group and the second thread group; and restarting the halted threads.
 40. The non-transitory machine-readable medium of claim 39, wherein performing the de-fragmentation of the SLM space includes migrating threads of two or more thread groups to create the contiguous space in the SLM space. 